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Influence of Atmospheric Slant Path on Geostationary Hyperspectral Infrared Sounder Radiance Simulations
Accurately simulating a geostationary hyperspectral infrared sounder is critical for quantitative applications. Traditional radiation simulations of such instruments often overlook the influence of slant observation geometry by using vertical profile assumption, leading to inadequate simulation accu...
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Published in: | Geophysical research letters 2024-12, Vol.51 (23), p.n/a |
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creator | Huang, Pengyun Li, Jun Min, Min Li, Zhenglong Di, Di Anantharaj, Valentine Ahn, Myoung‐Hwan |
description | Accurately simulating a geostationary hyperspectral infrared sounder is critical for quantitative applications. Traditional radiation simulations of such instruments often overlook the influence of slant observation geometry by using vertical profile assumption, leading to inadequate simulation accuracy. By using global atmospheric profiles with 1 km spatial resolution, the slant‐path effects on brightness temperature simulations are quantified. Experiments indicate that the slant geometry has less impact on longwave brightness temperature simulations and has a substantial impact on middle‐wave brightness temperature simulations. It may introduce 0.5 K (or more) uncertainty to brightness temperatures of water vapor absorption channels when the satellite zenith angle is greater than 45°. Considering the slant profile is recommended for quantitative applications of geostationary hyperspectral sounder data, such as sounding retrieval and data assimilation.
Plain Language Summary
Our research explored how the viewing angle of geostationary satellites affects the radiances taken by a special type of instrument called a geostationary hyperspectral infrared sounder. China, Europe, the United States, Japan, Korea, and India have been developing these instruments to help understand what is happening in the atmosphere by measuring infrared radiation. For simplicity, the traditional way is to assume that the atmospheric temperature and moisture profiles are vertical at a given field of view in quantitative applications. However, a slant path exists from Earth's footprint to the top of the atmosphere viewed by the satellite. What is the calculated radiance difference between the actual slant profile and the assumed vertical profile? Is the vertical assumption reasonable for applications? Our study investigated these questions and found that the angle at which the instrument views the atmosphere—the slant path—can impact the measurement accuracy, especially when the satellite zenith angle is greater than 40°. By studying this influence, we recommend that satellite users and researchers who use geostationary hyperspectral infrared sounder data consider the actual slant path for quantitative applications such as extracting moisture information and conducting data assimilation to improve high‐impact weather forecasts based on numerical weather predictions.
Key Points
Slant geometry may introduce more than 0.5 K uncertainty for water vapor absorption channels when the LZA |
doi_str_mv | 10.1029/2024GL110579 |
format | article |
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Plain Language Summary
Our research explored how the viewing angle of geostationary satellites affects the radiances taken by a special type of instrument called a geostationary hyperspectral infrared sounder. China, Europe, the United States, Japan, Korea, and India have been developing these instruments to help understand what is happening in the atmosphere by measuring infrared radiation. For simplicity, the traditional way is to assume that the atmospheric temperature and moisture profiles are vertical at a given field of view in quantitative applications. However, a slant path exists from Earth's footprint to the top of the atmosphere viewed by the satellite. What is the calculated radiance difference between the actual slant profile and the assumed vertical profile? Is the vertical assumption reasonable for applications? Our study investigated these questions and found that the angle at which the instrument views the atmosphere—the slant path—can impact the measurement accuracy, especially when the satellite zenith angle is greater than 40°. By studying this influence, we recommend that satellite users and researchers who use geostationary hyperspectral infrared sounder data consider the actual slant path for quantitative applications such as extracting moisture information and conducting data assimilation to improve high‐impact weather forecasts based on numerical weather predictions.
Key Points
Slant geometry may introduce more than 0.5 K uncertainty for water vapor absorption channels when the LZA is greater than 45°
Slant geometry greatly impacts quantitative applications, especially when the angle is greater than 60°
Considering the slant profile in profile retrieval and data assimilation is recommended</description><identifier>ISSN: 0094-8276</identifier><identifier>EISSN: 1944-8007</identifier><identifier>DOI: 10.1029/2024GL110579</identifier><language>eng</language><publisher>Washington: John Wiley & Sons, Inc</publisher><subject>Accuracy ; Atmosphere ; Atmospheric temperature ; Biological assimilation ; Brightness ; Brightness temperature ; Data assimilation ; Data collection ; Field of view ; geostationary hyperspectral infrared sounder ; Geostationary satellites ; I.R. radiation ; Infrared instruments ; Infrared radiation ; Measuring instruments ; Moisture ; Moisture profiles ; Numerical weather forecasting ; Radiance ; Radiation ; Radiation measurement ; Satellites ; Simulation ; slant observation geometry ; Spatial discrimination ; Spatial resolution ; Surface radiation temperature ; Synchronous satellites ; Temperature ; temperature and humidity profiles ; Temperature effects ; Vertical profiles ; Water temperature ; Water vapor ; Water vapour ; Weather ; Weather forecasting ; Zenith</subject><ispartof>Geophysical research letters, 2024-12, Vol.51 (23), p.n/a</ispartof><rights>2024. The Author(s).</rights><rights>2024. This article is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><orcidid>0000-0001-5504-9627 ; 0000-0003-0475-9690 ; 0000-0002-2044-5336 ; 0000-0003-1737-735X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1029%2F2024GL110579$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1029%2F2024GL110579$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,11514,11562,27924,27925,46052,46468,46476,46892</link.rule.ids></links><search><creatorcontrib>Huang, Pengyun</creatorcontrib><creatorcontrib>Li, Jun</creatorcontrib><creatorcontrib>Min, Min</creatorcontrib><creatorcontrib>Li, Zhenglong</creatorcontrib><creatorcontrib>Di, Di</creatorcontrib><creatorcontrib>Anantharaj, Valentine</creatorcontrib><creatorcontrib>Ahn, Myoung‐Hwan</creatorcontrib><title>Influence of Atmospheric Slant Path on Geostationary Hyperspectral Infrared Sounder Radiance Simulations</title><title>Geophysical research letters</title><description>Accurately simulating a geostationary hyperspectral infrared sounder is critical for quantitative applications. Traditional radiation simulations of such instruments often overlook the influence of slant observation geometry by using vertical profile assumption, leading to inadequate simulation accuracy. By using global atmospheric profiles with 1 km spatial resolution, the slant‐path effects on brightness temperature simulations are quantified. Experiments indicate that the slant geometry has less impact on longwave brightness temperature simulations and has a substantial impact on middle‐wave brightness temperature simulations. It may introduce 0.5 K (or more) uncertainty to brightness temperatures of water vapor absorption channels when the satellite zenith angle is greater than 45°. Considering the slant profile is recommended for quantitative applications of geostationary hyperspectral sounder data, such as sounding retrieval and data assimilation.
Plain Language Summary
Our research explored how the viewing angle of geostationary satellites affects the radiances taken by a special type of instrument called a geostationary hyperspectral infrared sounder. China, Europe, the United States, Japan, Korea, and India have been developing these instruments to help understand what is happening in the atmosphere by measuring infrared radiation. For simplicity, the traditional way is to assume that the atmospheric temperature and moisture profiles are vertical at a given field of view in quantitative applications. However, a slant path exists from Earth's footprint to the top of the atmosphere viewed by the satellite. What is the calculated radiance difference between the actual slant profile and the assumed vertical profile? Is the vertical assumption reasonable for applications? Our study investigated these questions and found that the angle at which the instrument views the atmosphere—the slant path—can impact the measurement accuracy, especially when the satellite zenith angle is greater than 40°. By studying this influence, we recommend that satellite users and researchers who use geostationary hyperspectral infrared sounder data consider the actual slant path for quantitative applications such as extracting moisture information and conducting data assimilation to improve high‐impact weather forecasts based on numerical weather predictions.
Key Points
Slant geometry may introduce more than 0.5 K uncertainty for water vapor absorption channels when the LZA is greater than 45°
Slant geometry greatly impacts quantitative applications, especially when the angle is greater than 60°
Considering the slant profile in profile retrieval and data assimilation is recommended</description><subject>Accuracy</subject><subject>Atmosphere</subject><subject>Atmospheric temperature</subject><subject>Biological assimilation</subject><subject>Brightness</subject><subject>Brightness temperature</subject><subject>Data assimilation</subject><subject>Data collection</subject><subject>Field of view</subject><subject>geostationary hyperspectral infrared sounder</subject><subject>Geostationary satellites</subject><subject>I.R. radiation</subject><subject>Infrared instruments</subject><subject>Infrared radiation</subject><subject>Measuring instruments</subject><subject>Moisture</subject><subject>Moisture profiles</subject><subject>Numerical weather forecasting</subject><subject>Radiance</subject><subject>Radiation</subject><subject>Radiation measurement</subject><subject>Satellites</subject><subject>Simulation</subject><subject>slant observation geometry</subject><subject>Spatial discrimination</subject><subject>Spatial resolution</subject><subject>Surface radiation temperature</subject><subject>Synchronous satellites</subject><subject>Temperature</subject><subject>temperature and humidity profiles</subject><subject>Temperature effects</subject><subject>Vertical profiles</subject><subject>Water temperature</subject><subject>Water vapor</subject><subject>Water vapour</subject><subject>Weather</subject><subject>Weather forecasting</subject><subject>Zenith</subject><issn>0094-8276</issn><issn>1944-8007</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>DOA</sourceid><recordid>eNpNkV9LwzAUxYMoOKdvfoCAz9ObpPn3OIZug4Gy6XNIk9R1dE1NW2Tf3m4T8ekeLvf-zoGD0D2BRwJUP1Gg2XxFCHCpL9CI6CybKAB5iUYAetBUimt007Y7AGDAyAhtl3VR9aF2AccCT7t9bJttSKXDm8rWHX6z3RbHGs9DbDvblbG26YAXhyaktgmuS7bCAyLZFDzexL72IeG19aU9Ijflvq9OX-0tuips1Ya73zlGHy_P77PFZPU6X86mq4mnlOoJz7xjzlqhHCsk54LIwkEulLaFyoVjHpSinstcgxKeSa9CTqRUSjAngLAxWp65PtqdaVK5HwKbaEtzWsT0aWzqSlcF4xRxToMOTotMKJbnnAYFnmW5BEqygfVwZjUpfvWh7cwu9qke4htGMsol4fzoSM9X32UVDn-WBMyxFPO_FDNfr4QaBPsB2n-AUQ</recordid><startdate>20241216</startdate><enddate>20241216</enddate><creator>Huang, Pengyun</creator><creator>Li, Jun</creator><creator>Min, Min</creator><creator>Li, Zhenglong</creator><creator>Di, Di</creator><creator>Anantharaj, Valentine</creator><creator>Ahn, Myoung‐Hwan</creator><general>John Wiley & Sons, Inc</general><general>Wiley</general><scope>24P</scope><scope>WIN</scope><scope>7TG</scope><scope>7TN</scope><scope>8FD</scope><scope>F1W</scope><scope>FR3</scope><scope>H8D</scope><scope>H96</scope><scope>KL.</scope><scope>KR7</scope><scope>L.G</scope><scope>L7M</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0001-5504-9627</orcidid><orcidid>https://orcid.org/0000-0003-0475-9690</orcidid><orcidid>https://orcid.org/0000-0002-2044-5336</orcidid><orcidid>https://orcid.org/0000-0003-1737-735X</orcidid></search><sort><creationdate>20241216</creationdate><title>Influence of Atmospheric Slant Path on Geostationary Hyperspectral Infrared Sounder Radiance Simulations</title><author>Huang, Pengyun ; Li, Jun ; Min, Min ; Li, Zhenglong ; Di, Di ; Anantharaj, Valentine ; Ahn, Myoung‐Hwan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-d2229-54dc3caa68c3f755617fc0b689af8b6c3d0882d57b9086d37d8eb1778863c6013</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Accuracy</topic><topic>Atmosphere</topic><topic>Atmospheric temperature</topic><topic>Biological assimilation</topic><topic>Brightness</topic><topic>Brightness temperature</topic><topic>Data assimilation</topic><topic>Data collection</topic><topic>Field of view</topic><topic>geostationary hyperspectral infrared sounder</topic><topic>Geostationary satellites</topic><topic>I.R. radiation</topic><topic>Infrared instruments</topic><topic>Infrared radiation</topic><topic>Measuring instruments</topic><topic>Moisture</topic><topic>Moisture profiles</topic><topic>Numerical weather forecasting</topic><topic>Radiance</topic><topic>Radiation</topic><topic>Radiation measurement</topic><topic>Satellites</topic><topic>Simulation</topic><topic>slant observation geometry</topic><topic>Spatial discrimination</topic><topic>Spatial resolution</topic><topic>Surface radiation temperature</topic><topic>Synchronous satellites</topic><topic>Temperature</topic><topic>temperature and humidity profiles</topic><topic>Temperature effects</topic><topic>Vertical profiles</topic><topic>Water temperature</topic><topic>Water vapor</topic><topic>Water vapour</topic><topic>Weather</topic><topic>Weather forecasting</topic><topic>Zenith</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Huang, Pengyun</creatorcontrib><creatorcontrib>Li, Jun</creatorcontrib><creatorcontrib>Min, Min</creatorcontrib><creatorcontrib>Li, Zhenglong</creatorcontrib><creatorcontrib>Di, Di</creatorcontrib><creatorcontrib>Anantharaj, Valentine</creatorcontrib><creatorcontrib>Ahn, Myoung‐Hwan</creatorcontrib><collection>Open Access: Wiley-Blackwell Open Access Journals</collection><collection>Wiley Online Library Free Content</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Oceanic Abstracts</collection><collection>Technology Research Database</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Geophysical research letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Huang, Pengyun</au><au>Li, Jun</au><au>Min, Min</au><au>Li, Zhenglong</au><au>Di, Di</au><au>Anantharaj, Valentine</au><au>Ahn, Myoung‐Hwan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Influence of Atmospheric Slant Path on Geostationary Hyperspectral Infrared Sounder Radiance Simulations</atitle><jtitle>Geophysical research letters</jtitle><date>2024-12-16</date><risdate>2024</risdate><volume>51</volume><issue>23</issue><epage>n/a</epage><issn>0094-8276</issn><eissn>1944-8007</eissn><abstract>Accurately simulating a geostationary hyperspectral infrared sounder is critical for quantitative applications. Traditional radiation simulations of such instruments often overlook the influence of slant observation geometry by using vertical profile assumption, leading to inadequate simulation accuracy. By using global atmospheric profiles with 1 km spatial resolution, the slant‐path effects on brightness temperature simulations are quantified. Experiments indicate that the slant geometry has less impact on longwave brightness temperature simulations and has a substantial impact on middle‐wave brightness temperature simulations. It may introduce 0.5 K (or more) uncertainty to brightness temperatures of water vapor absorption channels when the satellite zenith angle is greater than 45°. Considering the slant profile is recommended for quantitative applications of geostationary hyperspectral sounder data, such as sounding retrieval and data assimilation.
Plain Language Summary
Our research explored how the viewing angle of geostationary satellites affects the radiances taken by a special type of instrument called a geostationary hyperspectral infrared sounder. China, Europe, the United States, Japan, Korea, and India have been developing these instruments to help understand what is happening in the atmosphere by measuring infrared radiation. For simplicity, the traditional way is to assume that the atmospheric temperature and moisture profiles are vertical at a given field of view in quantitative applications. However, a slant path exists from Earth's footprint to the top of the atmosphere viewed by the satellite. What is the calculated radiance difference between the actual slant profile and the assumed vertical profile? Is the vertical assumption reasonable for applications? Our study investigated these questions and found that the angle at which the instrument views the atmosphere—the slant path—can impact the measurement accuracy, especially when the satellite zenith angle is greater than 40°. By studying this influence, we recommend that satellite users and researchers who use geostationary hyperspectral infrared sounder data consider the actual slant path for quantitative applications such as extracting moisture information and conducting data assimilation to improve high‐impact weather forecasts based on numerical weather predictions.
Key Points
Slant geometry may introduce more than 0.5 K uncertainty for water vapor absorption channels when the LZA is greater than 45°
Slant geometry greatly impacts quantitative applications, especially when the angle is greater than 60°
Considering the slant profile in profile retrieval and data assimilation is recommended</abstract><cop>Washington</cop><pub>John Wiley & Sons, Inc</pub><doi>10.1029/2024GL110579</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0001-5504-9627</orcidid><orcidid>https://orcid.org/0000-0003-0475-9690</orcidid><orcidid>https://orcid.org/0000-0002-2044-5336</orcidid><orcidid>https://orcid.org/0000-0003-1737-735X</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Accuracy Atmosphere Atmospheric temperature Biological assimilation Brightness Brightness temperature Data assimilation Data collection Field of view geostationary hyperspectral infrared sounder Geostationary satellites I.R. radiation Infrared instruments Infrared radiation Measuring instruments Moisture Moisture profiles Numerical weather forecasting Radiance Radiation Radiation measurement Satellites Simulation slant observation geometry Spatial discrimination Spatial resolution Surface radiation temperature Synchronous satellites Temperature temperature and humidity profiles Temperature effects Vertical profiles Water temperature Water vapor Water vapour Weather Weather forecasting Zenith |
title | Influence of Atmospheric Slant Path on Geostationary Hyperspectral Infrared Sounder Radiance Simulations |
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