Improvements to the AERIoe Thermodynamic Profile Retrieval Algorithm
Temperature and humidity profiles in the atmospheric boundary layer (i.e., from the surface to 3 km) can be retrieved from ground-based spectral infrared observations made by the atmospheric emitted radiance interferometer (AERI) at high temporal and moderate vertical resolution. However, the retrie...
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8576572/ |
| _version_ | 1819107616449626112 |
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| author | David D. Turner W. Greg Blumberg |
| author_facet | David D. Turner W. Greg Blumberg |
| author_sort | David D. Turner |
| collection | DOAJ |
| description | Temperature and humidity profiles in the atmospheric boundary layer (i.e., from the surface to 3 km) can be retrieved from ground-based spectral infrared observations made by the atmospheric emitted radiance interferometer (AERI) at high temporal and moderate vertical resolution. However, the retrieval is an ill-posed problem, and thus there are multiple thermodynamic solutions that might satisfy the observed radiances. Previous work developed a physical-iterative method called AERIoe that retrieved temperature and water vapor mixing ratio profiles from these radiance observations in both clear and cloudy conditions. The AERIoe algorithm was modified to enforce two physical constraints, namely that the derived relative humidity must be less than 100% and that the potential temperature must be monotonically increasing with height above some thin potentially subadiabatic layer after each iteration. Furthermore, additional observations including in situ surface meteorology, numerical weather prediction model output, microwave brightness temperatures, and partial profiles of water vapor from a Raman lidar were incorporated into the observation vector of the retrieval along with the infrared radiance observations. The addition of these new observations markedly improved the accuracy of the temperature profiles, especially above 2 km, and the water vapor profiles relative to radiosondes. These improvements are seen using cases from the tropics, mid-latitudes, and Arctic. |
| first_indexed | 2024-12-22T02:56:52Z |
| format | Article |
| id | doaj.art-8bd2f9efd1344760a1913d7a5023dc3e |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-12-22T02:56:52Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-8bd2f9efd1344760a1913d7a5023dc3e2022-12-21T18:41:15ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352019-01-011251339135410.1109/JSTARS.2018.28749688576572Improvements to the AERIoe Thermodynamic Profile Retrieval AlgorithmDavid D. Turner0https://orcid.org/0000-0003-1097-897XW. Greg Blumberg1https://orcid.org/0000-0001-7620-7962National Oceanic and Atmospheric Administration Earth System Research Laboratory, Boulder, CO, USACooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, OK, USATemperature and humidity profiles in the atmospheric boundary layer (i.e., from the surface to 3 km) can be retrieved from ground-based spectral infrared observations made by the atmospheric emitted radiance interferometer (AERI) at high temporal and moderate vertical resolution. However, the retrieval is an ill-posed problem, and thus there are multiple thermodynamic solutions that might satisfy the observed radiances. Previous work developed a physical-iterative method called AERIoe that retrieved temperature and water vapor mixing ratio profiles from these radiance observations in both clear and cloudy conditions. The AERIoe algorithm was modified to enforce two physical constraints, namely that the derived relative humidity must be less than 100% and that the potential temperature must be monotonically increasing with height above some thin potentially subadiabatic layer after each iteration. Furthermore, additional observations including in situ surface meteorology, numerical weather prediction model output, microwave brightness temperatures, and partial profiles of water vapor from a Raman lidar were incorporated into the observation vector of the retrieval along with the infrared radiance observations. The addition of these new observations markedly improved the accuracy of the temperature profiles, especially above 2 km, and the water vapor profiles relative to radiosondes. These improvements are seen using cases from the tropics, mid-latitudes, and Arctic.https://ieeexplore.ieee.org/document/8576572/Atmospheric measurementsinfrared radiometryremote sensing |
| spellingShingle | David D. Turner W. Greg Blumberg Improvements to the AERIoe Thermodynamic Profile Retrieval Algorithm IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Atmospheric measurements infrared radiometry remote sensing |
| title | Improvements to the AERIoe Thermodynamic Profile Retrieval Algorithm |
| title_full | Improvements to the AERIoe Thermodynamic Profile Retrieval Algorithm |
| title_fullStr | Improvements to the AERIoe Thermodynamic Profile Retrieval Algorithm |
| title_full_unstemmed | Improvements to the AERIoe Thermodynamic Profile Retrieval Algorithm |
| title_short | Improvements to the AERIoe Thermodynamic Profile Retrieval Algorithm |
| title_sort | improvements to the aerioe thermodynamic profile retrieval algorithm |
| topic | Atmospheric measurements infrared radiometry remote sensing |
| url | https://ieeexplore.ieee.org/document/8576572/ |
| work_keys_str_mv | AT daviddturner improvementstotheaerioethermodynamicprofileretrievalalgorithm AT wgregblumberg improvementstotheaerioethermodynamicprofileretrievalalgorithm |