The NCAR Airborne 94-GHz Cloud Radar: Calibration and Data Processing
The 94-GHz airborne HIAPER Cloud Radar (HCR) has been deployed in three major field campaigns, sampling clouds over the Pacific between California and Hawaii (2015), over the cold waters of the Southern Ocean (2018), and characterizing tropical convection in the Western Caribbean and Pacific waters...
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2021
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Online Access: | https://doi.org/10.3390/data6060066 https://doaj.org/article/3174425c0f354865a8bd8ec9a556e3a6 |
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fttriple:oai:gotriple.eu:oai:doaj.org/article:3174425c0f354865a8bd8ec9a556e3a6 2023-05-15T18:25:32+02:00 The NCAR Airborne 94-GHz Cloud Radar: Calibration and Data Processing Ulrike Romatschke Michael Dixon Peisang Tsai Eric Loew Jothiram Vivekanandan Jonathan Emmett Robert Rilling 2021-06-01 https://doi.org/10.3390/data6060066 https://doaj.org/article/3174425c0f354865a8bd8ec9a556e3a6 en eng MDPI AG doi:10.3390/data6060066 2306-5729 https://doaj.org/article/3174425c0f354865a8bd8ec9a556e3a6 undefined Data, Vol 6, Iss 66, p 66 (2021) radar cloud physics reflectivity radial velocity info geo Journal Article https://vocabularies.coar-repositories.org/resource_types/c_6501/ 2021 fttriple https://doi.org/10.3390/data6060066 2023-01-22T19:34:31Z The 94-GHz airborne HIAPER Cloud Radar (HCR) has been deployed in three major field campaigns, sampling clouds over the Pacific between California and Hawaii (2015), over the cold waters of the Southern Ocean (2018), and characterizing tropical convection in the Western Caribbean and Pacific waters off Panama and Costa Rica (2019). An extensive set of quality assurance and quality control procedures were developed and applied to all collected data. Engineering measurements yielded calibration characteristics for the antenna, reflector, and radome, which were applied during flight, to produce the radar moments in real-time. Temperature changes in the instrument during flight affect the receiver gains, leading to some bias. Post project, we estimate the temperature-induced gain errors and apply gain corrections to improve the quality of the data. The reflectivity calibration is monitored by comparing sea surface cross-section measurements against theoretically calculated model values. These comparisons indicate that the HCR is calibrated to within 1–2 dB of the theory. A radar echo classification algorithm was developed to identify “cloud echo” and distinguish it from artifacts. Model reanalysis data and digital terrain elevation data were interpolated to the time-range grid of the radar data, to provide an environmental reference. Article in Journal/Newspaper Southern Ocean Unknown Pacific Southern Ocean Data 6 6 66 |
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English |
topic |
radar cloud physics reflectivity radial velocity info geo |
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radar cloud physics reflectivity radial velocity info geo Ulrike Romatschke Michael Dixon Peisang Tsai Eric Loew Jothiram Vivekanandan Jonathan Emmett Robert Rilling The NCAR Airborne 94-GHz Cloud Radar: Calibration and Data Processing |
topic_facet |
radar cloud physics reflectivity radial velocity info geo |
description |
The 94-GHz airborne HIAPER Cloud Radar (HCR) has been deployed in three major field campaigns, sampling clouds over the Pacific between California and Hawaii (2015), over the cold waters of the Southern Ocean (2018), and characterizing tropical convection in the Western Caribbean and Pacific waters off Panama and Costa Rica (2019). An extensive set of quality assurance and quality control procedures were developed and applied to all collected data. Engineering measurements yielded calibration characteristics for the antenna, reflector, and radome, which were applied during flight, to produce the radar moments in real-time. Temperature changes in the instrument during flight affect the receiver gains, leading to some bias. Post project, we estimate the temperature-induced gain errors and apply gain corrections to improve the quality of the data. The reflectivity calibration is monitored by comparing sea surface cross-section measurements against theoretically calculated model values. These comparisons indicate that the HCR is calibrated to within 1–2 dB of the theory. A radar echo classification algorithm was developed to identify “cloud echo” and distinguish it from artifacts. Model reanalysis data and digital terrain elevation data were interpolated to the time-range grid of the radar data, to provide an environmental reference. |
format |
Article in Journal/Newspaper |
author |
Ulrike Romatschke Michael Dixon Peisang Tsai Eric Loew Jothiram Vivekanandan Jonathan Emmett Robert Rilling |
author_facet |
Ulrike Romatschke Michael Dixon Peisang Tsai Eric Loew Jothiram Vivekanandan Jonathan Emmett Robert Rilling |
author_sort |
Ulrike Romatschke |
title |
The NCAR Airborne 94-GHz Cloud Radar: Calibration and Data Processing |
title_short |
The NCAR Airborne 94-GHz Cloud Radar: Calibration and Data Processing |
title_full |
The NCAR Airborne 94-GHz Cloud Radar: Calibration and Data Processing |
title_fullStr |
The NCAR Airborne 94-GHz Cloud Radar: Calibration and Data Processing |
title_full_unstemmed |
The NCAR Airborne 94-GHz Cloud Radar: Calibration and Data Processing |
title_sort |
ncar airborne 94-ghz cloud radar: calibration and data processing |
publisher |
MDPI AG |
publishDate |
2021 |
url |
https://doi.org/10.3390/data6060066 https://doaj.org/article/3174425c0f354865a8bd8ec9a556e3a6 |
geographic |
Pacific Southern Ocean |
geographic_facet |
Pacific Southern Ocean |
genre |
Southern Ocean |
genre_facet |
Southern Ocean |
op_source |
Data, Vol 6, Iss 66, p 66 (2021) |
op_relation |
doi:10.3390/data6060066 2306-5729 https://doaj.org/article/3174425c0f354865a8bd8ec9a556e3a6 |
op_rights |
undefined |
op_doi |
https://doi.org/10.3390/data6060066 |
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6 |
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6 |
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66 |
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