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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Published in:Data
Main Authors: Ulrike Romatschke, Michael Dixon, Peisang Tsai, Eric Loew, Jothiram Vivekanandan, Jonathan Emmett, Robert Rilling
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2021
Subjects:
Online Access:https://doi.org/10.3390/data6060066
https://doaj.org/article/3174425c0f354865a8bd8ec9a556e3a6
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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
collection Unknown
container_issue 6
container_start_page 66
container_title Data
container_volume 6
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
genre Southern Ocean
genre_facet Southern Ocean
geographic Pacific
Southern Ocean
geographic_facet Pacific
Southern Ocean
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op_source Data, Vol 6, Iss 66, p 66 (2021)
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spelling fttriple:oai:gotriple.eu:oai:doaj.org/article:3174425c0f354865a8bd8ec9a556e3a6 2025-01-17T00:56:35+00:00 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
spellingShingle 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
title 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_short The NCAR Airborne 94-GHz Cloud Radar: Calibration and Data Processing
title_sort ncar airborne 94-ghz cloud radar: calibration and data processing
topic radar
cloud physics
reflectivity
radial velocity
info
geo
topic_facet radar
cloud physics
reflectivity
radial velocity
info
geo
url https://doi.org/10.3390/data6060066
https://doaj.org/article/3174425c0f354865a8bd8ec9a556e3a6