Pub Date : 2024-02-12DOI: 10.1175/jtech-d-23-0014.1
Je-Yuan Hsu
A new rotating axes method (RAM) is developed to improve the vertical resolution of the horizontal current velocity measurements u at EM-APEX floats. Unlike the traditional harmonic fitting method (HFM), which yields u averaged in 50-s intervals, RAM decodes and interprets 1-Hz measurements of horizontal seawater velocity ũ, and averages ũ in 12-s windows for removing wind waves with a typical peak frequency ∼ 0.12 Hz. Estimates of u from RAM agree with those from HFM but with a higher vertical resolution of ∼1.5 m, 4 times better than HFM. Note that extracting float signals due to seawater motion needs to assume slow-varying voltage offset ΔΦoffset. The typical variations of estimated ΔΦoffset do not affect the results of u significantly. Estimates of u are excluded when ΔΦoffset fluctuates strongly in time and scatter significantly. RAM is applied to float measurements taken near Mien-Hua Canyon, Taiwan. Composite vertical shear spectra Ψ computed using u from RAM exhibit a spectral slope of −1, as expected for the saturated internal waves in the vertical fine scale range. The RAM provides EM-APEX float’s horizontal velocity measurements into fine vertical scales and will help improve our understanding of energy cascade from internal wave breaking and shear instability into turbulence mixing.
为提高 EM-APEX 浮筒水平流速测量值 u 的垂直分辨率，开发了一种新的旋转轴法（RAM）。与传统的谐波拟合法（HFM）不同，RAM 对 1 赫兹的水平海水流速ũ 测量值进行解码和解释，并在 12 秒的窗口内平均ũ，以去除典型峰值频率∼ 0.12 赫兹的风浪。RAM 对 u 的估计值与 HFM 的估计值一致，但垂直分辨率更高，为 1.5 米，是 HFM 的 4 倍。需要注意的是，提取海水运动引起的浮标信号需要假定电压偏移 ΔΦoffset 变化缓慢。估计 ΔΦoffset 的典型变化对 u 的结果影响不大。当 ΔΦoffset 随时间剧烈波动且散差较大时，u 的估计值将被排除。RAM 应用于台湾面华峡谷附近的浮漂测量。使用 RAM 中的 u 计算出的综合垂直剪切谱Ψ 显示出谱斜率为-1，与垂直细尺度范围内饱和内波的预期一致。RAM提供了EM-APEX浮漂在垂直细尺度的水平速度测量，将有助于提高我们对内波破碎和剪切不稳定性到湍流混合的能量级联的理解。
{"title":"A New Rotating Axes Method for Processing High-Resolution Horizontal Velocity Measurements on EM-APEX floats","authors":"Je-Yuan Hsu","doi":"10.1175/jtech-d-23-0014.1","DOIUrl":"https://doi.org/10.1175/jtech-d-23-0014.1","url":null,"abstract":"\u0000A new rotating axes method (RAM) is developed to improve the vertical resolution of the horizontal current velocity measurements u at EM-APEX floats. Unlike the traditional harmonic fitting method (HFM), which yields u averaged in 50-s intervals, RAM decodes and interprets 1-Hz measurements of horizontal seawater velocity ũ, and averages ũ in 12-s windows for removing wind waves with a typical peak frequency ∼ 0.12 Hz. Estimates of u from RAM agree with those from HFM but with a higher vertical resolution of ∼1.5 m, 4 times better than HFM. Note that extracting float signals due to seawater motion needs to assume slow-varying voltage offset ΔΦoffset. The typical variations of estimated ΔΦoffset do not affect the results of u significantly. Estimates of u are excluded when ΔΦoffset fluctuates strongly in time and scatter significantly. RAM is applied to float measurements taken near Mien-Hua Canyon, Taiwan. Composite vertical shear spectra Ψ computed using u from RAM exhibit a spectral slope of −1, as expected for the saturated internal waves in the vertical fine scale range. The RAM provides EM-APEX float’s horizontal velocity measurements into fine vertical scales and will help improve our understanding of energy cascade from internal wave breaking and shear instability into turbulence mixing.","PeriodicalId":507668,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139784477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-12DOI: 10.1175/jtech-d-23-0014.1
Je-Yuan Hsu
A new rotating axes method (RAM) is developed to improve the vertical resolution of the horizontal current velocity measurements u at EM-APEX floats. Unlike the traditional harmonic fitting method (HFM), which yields u averaged in 50-s intervals, RAM decodes and interprets 1-Hz measurements of horizontal seawater velocity ũ, and averages ũ in 12-s windows for removing wind waves with a typical peak frequency ∼ 0.12 Hz. Estimates of u from RAM agree with those from HFM but with a higher vertical resolution of ∼1.5 m, 4 times better than HFM. Note that extracting float signals due to seawater motion needs to assume slow-varying voltage offset ΔΦoffset. The typical variations of estimated ΔΦoffset do not affect the results of u significantly. Estimates of u are excluded when ΔΦoffset fluctuates strongly in time and scatter significantly. RAM is applied to float measurements taken near Mien-Hua Canyon, Taiwan. Composite vertical shear spectra Ψ computed using u from RAM exhibit a spectral slope of −1, as expected for the saturated internal waves in the vertical fine scale range. The RAM provides EM-APEX float’s horizontal velocity measurements into fine vertical scales and will help improve our understanding of energy cascade from internal wave breaking and shear instability into turbulence mixing.
为提高 EM-APEX 浮筒水平流速测量值 u 的垂直分辨率，开发了一种新的旋转轴法（RAM）。与传统的谐波拟合法（HFM）不同，RAM 对 1 赫兹的水平海水流速ũ 测量值进行解码和解释，并在 12 秒的窗口内平均ũ，以去除典型峰值频率∼ 0.12 赫兹的风浪。RAM 对 u 的估计值与 HFM 的估计值一致，但垂直分辨率更高，为 1.5 米，是 HFM 的 4 倍。需要注意的是，提取海水运动引起的浮标信号需要假定电压偏移 ΔΦoffset 变化缓慢。估计 ΔΦoffset 的典型变化对 u 的结果影响不大。当 ΔΦoffset 随时间剧烈波动且散差较大时，u 的估计值将被排除。RAM 应用于台湾面华峡谷附近的浮漂测量。使用 RAM 中的 u 计算出的综合垂直剪切谱Ψ 显示出谱斜率为-1，与垂直细尺度范围内饱和内波的预期一致。RAM提供了EM-APEX浮漂在垂直细尺度的水平速度测量，将有助于提高我们对内波破碎和剪切不稳定性到湍流混合的能量级联的理解。
{"title":"A New Rotating Axes Method for Processing High-Resolution Horizontal Velocity Measurements on EM-APEX floats","authors":"Je-Yuan Hsu","doi":"10.1175/jtech-d-23-0014.1","DOIUrl":"https://doi.org/10.1175/jtech-d-23-0014.1","url":null,"abstract":"\u0000A new rotating axes method (RAM) is developed to improve the vertical resolution of the horizontal current velocity measurements u at EM-APEX floats. Unlike the traditional harmonic fitting method (HFM), which yields u averaged in 50-s intervals, RAM decodes and interprets 1-Hz measurements of horizontal seawater velocity ũ, and averages ũ in 12-s windows for removing wind waves with a typical peak frequency ∼ 0.12 Hz. Estimates of u from RAM agree with those from HFM but with a higher vertical resolution of ∼1.5 m, 4 times better than HFM. Note that extracting float signals due to seawater motion needs to assume slow-varying voltage offset ΔΦoffset. The typical variations of estimated ΔΦoffset do not affect the results of u significantly. Estimates of u are excluded when ΔΦoffset fluctuates strongly in time and scatter significantly. RAM is applied to float measurements taken near Mien-Hua Canyon, Taiwan. Composite vertical shear spectra Ψ computed using u from RAM exhibit a spectral slope of −1, as expected for the saturated internal waves in the vertical fine scale range. The RAM provides EM-APEX float’s horizontal velocity measurements into fine vertical scales and will help improve our understanding of energy cascade from internal wave breaking and shear instability into turbulence mixing.","PeriodicalId":507668,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139844263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-09DOI: 10.1175/jtech-d-23-0001.1
J. Schlosser, Ryan Bennett, Brian Cairns, Gao Chen, B. Collister, J. Hair, Michael Jones, M. Shook, A. Sorooshian, K. Thornhill, L. Ziemba, S. Stamnes
Suborbital (e.g., airborne) campaigns that carry advanced remote sensing and in situ payloads provide detailed observations of atmospheric processes, but can be challenging to use when it is necessary to geographically collocate data from multiple platforms that make repeated observations of a given geographic location at different altitudes. This study reports on a data collocation algorithm that maximizes the volume of collocated data from two coordinated suborbital platforms and demonstrates its value using data from the NASA Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) suborbital mission. A robust data collocation algorithm is critical for the success of the ACTIVATE mission goal to develop new and improved remote sensing algorithms, and quantify their performance. We demonstrate the value of these collocated data to quantify the performance of a recently developed vertically-resolved lidar + polarimeter-derived aerosol particle number concentration (Na) product, resulting in a range-normalized mean absolute deviation (NMAD) of 9% compared to in situ measurements. We also show that this collocation algorithm increases the volume of collocated ACTIVATE data by 21% compared to using only nearest neighbor finding algorithms alone. Additional to the benefits demonstrated within this study, the data files and routines produced by this algorithm have solved both the critical collocation and the collocation application steps for researchers who require collocated data for their own studies. This freely available and open source collocation algorithm can be applied to future suborbital campaigns that, like ACTIVATE, use multiple platforms to conduct coordinated observations, e.g., a remote sensing aircraft together with in situ data collected from suborbital platforms.
{"title":"Maximizing the volume of collocated data from two coordinated suborbital platforms","authors":"J. Schlosser, Ryan Bennett, Brian Cairns, Gao Chen, B. Collister, J. Hair, Michael Jones, M. Shook, A. Sorooshian, K. Thornhill, L. Ziemba, S. Stamnes","doi":"10.1175/jtech-d-23-0001.1","DOIUrl":"https://doi.org/10.1175/jtech-d-23-0001.1","url":null,"abstract":"\u0000Suborbital (e.g., airborne) campaigns that carry advanced remote sensing and in situ payloads provide detailed observations of atmospheric processes, but can be challenging to use when it is necessary to geographically collocate data from multiple platforms that make repeated observations of a given geographic location at different altitudes. This study reports on a data collocation algorithm that maximizes the volume of collocated data from two coordinated suborbital platforms and demonstrates its value using data from the NASA Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) suborbital mission. A robust data collocation algorithm is critical for the success of the ACTIVATE mission goal to develop new and improved remote sensing algorithms, and quantify their performance. We demonstrate the value of these collocated data to quantify the performance of a recently developed vertically-resolved lidar + polarimeter-derived aerosol particle number concentration (Na) product, resulting in a range-normalized mean absolute deviation (NMAD) of 9% compared to in situ measurements. We also show that this collocation algorithm increases the volume of collocated ACTIVATE data by 21% compared to using only nearest neighbor finding algorithms alone. Additional to the benefits demonstrated within this study, the data files and routines produced by this algorithm have solved both the critical collocation and the collocation application steps for researchers who require collocated data for their own studies. This freely available and open source collocation algorithm can be applied to future suborbital campaigns that, like ACTIVATE, use multiple platforms to conduct coordinated observations, e.g., a remote sensing aircraft together with in situ data collected from suborbital platforms.","PeriodicalId":507668,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139789191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-09DOI: 10.1175/jtech-d-23-0001.1
J. Schlosser, Ryan Bennett, Brian Cairns, Gao Chen, B. Collister, J. Hair, Michael Jones, M. Shook, A. Sorooshian, K. Thornhill, L. Ziemba, S. Stamnes
Suborbital (e.g., airborne) campaigns that carry advanced remote sensing and in situ payloads provide detailed observations of atmospheric processes, but can be challenging to use when it is necessary to geographically collocate data from multiple platforms that make repeated observations of a given geographic location at different altitudes. This study reports on a data collocation algorithm that maximizes the volume of collocated data from two coordinated suborbital platforms and demonstrates its value using data from the NASA Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) suborbital mission. A robust data collocation algorithm is critical for the success of the ACTIVATE mission goal to develop new and improved remote sensing algorithms, and quantify their performance. We demonstrate the value of these collocated data to quantify the performance of a recently developed vertically-resolved lidar + polarimeter-derived aerosol particle number concentration (Na) product, resulting in a range-normalized mean absolute deviation (NMAD) of 9% compared to in situ measurements. We also show that this collocation algorithm increases the volume of collocated ACTIVATE data by 21% compared to using only nearest neighbor finding algorithms alone. Additional to the benefits demonstrated within this study, the data files and routines produced by this algorithm have solved both the critical collocation and the collocation application steps for researchers who require collocated data for their own studies. This freely available and open source collocation algorithm can be applied to future suborbital campaigns that, like ACTIVATE, use multiple platforms to conduct coordinated observations, e.g., a remote sensing aircraft together with in situ data collected from suborbital platforms.
{"title":"Maximizing the volume of collocated data from two coordinated suborbital platforms","authors":"J. Schlosser, Ryan Bennett, Brian Cairns, Gao Chen, B. Collister, J. Hair, Michael Jones, M. Shook, A. Sorooshian, K. Thornhill, L. Ziemba, S. Stamnes","doi":"10.1175/jtech-d-23-0001.1","DOIUrl":"https://doi.org/10.1175/jtech-d-23-0001.1","url":null,"abstract":"\u0000Suborbital (e.g., airborne) campaigns that carry advanced remote sensing and in situ payloads provide detailed observations of atmospheric processes, but can be challenging to use when it is necessary to geographically collocate data from multiple platforms that make repeated observations of a given geographic location at different altitudes. This study reports on a data collocation algorithm that maximizes the volume of collocated data from two coordinated suborbital platforms and demonstrates its value using data from the NASA Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) suborbital mission. A robust data collocation algorithm is critical for the success of the ACTIVATE mission goal to develop new and improved remote sensing algorithms, and quantify their performance. We demonstrate the value of these collocated data to quantify the performance of a recently developed vertically-resolved lidar + polarimeter-derived aerosol particle number concentration (Na) product, resulting in a range-normalized mean absolute deviation (NMAD) of 9% compared to in situ measurements. We also show that this collocation algorithm increases the volume of collocated ACTIVATE data by 21% compared to using only nearest neighbor finding algorithms alone. Additional to the benefits demonstrated within this study, the data files and routines produced by this algorithm have solved both the critical collocation and the collocation application steps for researchers who require collocated data for their own studies. This freely available and open source collocation algorithm can be applied to future suborbital campaigns that, like ACTIVATE, use multiple platforms to conduct coordinated observations, e.g., a remote sensing aircraft together with in situ data collected from suborbital platforms.","PeriodicalId":507668,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139849028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-01DOI: 10.1175/jtech-d-23-0062.1
Seungtae Lee, Yang-Ki Cho, Jihun Jung, Byoung-Ju Choi, Young-Ho Kim, Sangil Kim
The North Pacific is divided into different regions based on ocean currents and sea surface temperature (SST) distribution. Data assimilation is a useful tool for generating accurate ocean estimates because of the limited availability of observational data. This study compared the performances of two data assimilation methods, ensemble optimal interpolation (EnOI) and ensemble Kalman filter (EnKF), in various North Pacific subregions using an ocean model configured with the Regional Ocean Modeling System (ROMS). Both methods assimilated spaceborne SST observations, and the simulation results varied by subregion. The study found that EnKF and EnOI methods performed better than the control model in all regions when compared against satellite SST. EnOI reproduced SST as well as EnKF and required fewer computational resources. However, EnOI performed worse than the control model at sea surface height (SSH) in the equatorial region, while EnKF’s performance improved. This was due to the crushed mean state in the EnOI, which used long-term historical data as an ensemble member. El Niño–Southern Oscillation at the equator drove substantial interannual variability that crushed the ensemble mean of SSH in the EnOI. It is crucial to use a suitable assimilation method for the target area, considering the regional properties of ocean variables. Otherwise, the performance of the assimilated model may be even worse than that of the control model. While EnKF is better suited for regions with high variability in ocean variables, EnOI requires fewer computational resources. Thus, it is crucial to use a suitable assimilation method for accurately predicting and understanding the dynamics of the North Pacific.
{"title":"Regional Comparison of Performance between EnKF and EnOI in the North Pacific","authors":"Seungtae Lee, Yang-Ki Cho, Jihun Jung, Byoung-Ju Choi, Young-Ho Kim, Sangil Kim","doi":"10.1175/jtech-d-23-0062.1","DOIUrl":"https://doi.org/10.1175/jtech-d-23-0062.1","url":null,"abstract":"\u0000The North Pacific is divided into different regions based on ocean currents and sea surface temperature (SST) distribution. Data assimilation is a useful tool for generating accurate ocean estimates because of the limited availability of observational data. This study compared the performances of two data assimilation methods, ensemble optimal interpolation (EnOI) and ensemble Kalman filter (EnKF), in various North Pacific subregions using an ocean model configured with the Regional Ocean Modeling System (ROMS). Both methods assimilated spaceborne SST observations, and the simulation results varied by subregion. The study found that EnKF and EnOI methods performed better than the control model in all regions when compared against satellite SST. EnOI reproduced SST as well as EnKF and required fewer computational resources. However, EnOI performed worse than the control model at sea surface height (SSH) in the equatorial region, while EnKF’s performance improved. This was due to the crushed mean state in the EnOI, which used long-term historical data as an ensemble member. El Niño–Southern Oscillation at the equator drove substantial interannual variability that crushed the ensemble mean of SSH in the EnOI. It is crucial to use a suitable assimilation method for the target area, considering the regional properties of ocean variables. Otherwise, the performance of the assimilated model may be even worse than that of the control model. While EnKF is better suited for regions with high variability in ocean variables, EnOI requires fewer computational resources. Thus, it is crucial to use a suitable assimilation method for accurately predicting and understanding the dynamics of the North Pacific.","PeriodicalId":507668,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139892241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-01DOI: 10.1175/jtech-d-23-0062.1
Seungtae Lee, Yang-Ki Cho, Jihun Jung, Byoung-Ju Choi, Young-Ho Kim, Sangil Kim
The North Pacific is divided into different regions based on ocean currents and sea surface temperature (SST) distribution. Data assimilation is a useful tool for generating accurate ocean estimates because of the limited availability of observational data. This study compared the performances of two data assimilation methods, ensemble optimal interpolation (EnOI) and ensemble Kalman filter (EnKF), in various North Pacific subregions using an ocean model configured with the Regional Ocean Modeling System (ROMS). Both methods assimilated spaceborne SST observations, and the simulation results varied by subregion. The study found that EnKF and EnOI methods performed better than the control model in all regions when compared against satellite SST. EnOI reproduced SST as well as EnKF and required fewer computational resources. However, EnOI performed worse than the control model at sea surface height (SSH) in the equatorial region, while EnKF’s performance improved. This was due to the crushed mean state in the EnOI, which used long-term historical data as an ensemble member. El Niño–Southern Oscillation at the equator drove substantial interannual variability that crushed the ensemble mean of SSH in the EnOI. It is crucial to use a suitable assimilation method for the target area, considering the regional properties of ocean variables. Otherwise, the performance of the assimilated model may be even worse than that of the control model. While EnKF is better suited for regions with high variability in ocean variables, EnOI requires fewer computational resources. Thus, it is crucial to use a suitable assimilation method for accurately predicting and understanding the dynamics of the North Pacific.
{"title":"Regional Comparison of Performance between EnKF and EnOI in the North Pacific","authors":"Seungtae Lee, Yang-Ki Cho, Jihun Jung, Byoung-Ju Choi, Young-Ho Kim, Sangil Kim","doi":"10.1175/jtech-d-23-0062.1","DOIUrl":"https://doi.org/10.1175/jtech-d-23-0062.1","url":null,"abstract":"\u0000The North Pacific is divided into different regions based on ocean currents and sea surface temperature (SST) distribution. Data assimilation is a useful tool for generating accurate ocean estimates because of the limited availability of observational data. This study compared the performances of two data assimilation methods, ensemble optimal interpolation (EnOI) and ensemble Kalman filter (EnKF), in various North Pacific subregions using an ocean model configured with the Regional Ocean Modeling System (ROMS). Both methods assimilated spaceborne SST observations, and the simulation results varied by subregion. The study found that EnKF and EnOI methods performed better than the control model in all regions when compared against satellite SST. EnOI reproduced SST as well as EnKF and required fewer computational resources. However, EnOI performed worse than the control model at sea surface height (SSH) in the equatorial region, while EnKF’s performance improved. This was due to the crushed mean state in the EnOI, which used long-term historical data as an ensemble member. El Niño–Southern Oscillation at the equator drove substantial interannual variability that crushed the ensemble mean of SSH in the EnOI. It is crucial to use a suitable assimilation method for the target area, considering the regional properties of ocean variables. Otherwise, the performance of the assimilated model may be even worse than that of the control model. While EnKF is better suited for regions with high variability in ocean variables, EnOI requires fewer computational resources. Thus, it is crucial to use a suitable assimilation method for accurately predicting and understanding the dynamics of the North Pacific.","PeriodicalId":507668,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139832122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-31DOI: 10.1175/jtech-d-23-0069.1
L. Gelinas, J. Hecht, R. J. Rudy
The OH airglow layer is a persistent feature of the Earth’s upper mesosphere, centered near 87 km altitude, that can be perturbed by atmospheric gravity waves (AGWs) and instabilities. While ground-based airglow imaging has been used to study these perturbations locally, this technique is limited by tropospheric weather. Space-based remote sensing provides a platform to measure these processes globally. In addition, portions of the OH airglow band span an atmospheric window, allowing airglow illumination of the ground for imaging of nighttime clouds and Earth terrain features. The Near Infrared Airglow Camera (NIRAC) images the airglow at 1.6 μm and while deployed to the International Space Station (ISS) from 05/2019 – 11/2021 demonstrated these applications. The camera uses a patented motion-compensation system with a custom rectilinear lens that allows multi-second, nearly smear-free imaging (∼<1.5 pixel) at a ground pixel resolution of ∼83 m. With a ∼ 170 x 170 km ground swath, NIRAC acquires overlapping images at a 7-10 s cadence. Parallax considerations enable detection of both AGWs and instabilities in the airglow, and scenes can be analyzed for terrain and cloud height. NIRAC also has a short-exposure daytime mode for cloud and ground imagery. This study describes NIRAC and its operations on the ISS and presents imagery examples of Earth terrain and surface phenomenology (such as fires), cloud imagery at all Moon phases day and night, and the nighttime detection of AGWs and instabilities above 80 km altitude.
{"title":"The Near Infrared Airglow Camera on the International Space Station","authors":"L. Gelinas, J. Hecht, R. J. Rudy","doi":"10.1175/jtech-d-23-0069.1","DOIUrl":"https://doi.org/10.1175/jtech-d-23-0069.1","url":null,"abstract":"\u0000The OH airglow layer is a persistent feature of the Earth’s upper mesosphere, centered near 87 km altitude, that can be perturbed by atmospheric gravity waves (AGWs) and instabilities. While ground-based airglow imaging has been used to study these perturbations locally, this technique is limited by tropospheric weather. Space-based remote sensing provides a platform to measure these processes globally. In addition, portions of the OH airglow band span an atmospheric window, allowing airglow illumination of the ground for imaging of nighttime clouds and Earth terrain features. The Near Infrared Airglow Camera (NIRAC) images the airglow at 1.6 μm and while deployed to the International Space Station (ISS) from 05/2019 – 11/2021 demonstrated these applications. The camera uses a patented motion-compensation system with a custom rectilinear lens that allows multi-second, nearly smear-free imaging (∼<1.5 pixel) at a ground pixel resolution of ∼83 m. With a ∼ 170 x 170 km ground swath, NIRAC acquires overlapping images at a 7-10 s cadence. Parallax considerations enable detection of both AGWs and instabilities in the airglow, and scenes can be analyzed for terrain and cloud height. NIRAC also has a short-exposure daytime mode for cloud and ground imagery. This study describes NIRAC and its operations on the ISS and presents imagery examples of Earth terrain and surface phenomenology (such as fires), cloud imagery at all Moon phases day and night, and the nighttime detection of AGWs and instabilities above 80 km altitude.","PeriodicalId":507668,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140478372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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