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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| Format: | Article |
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MDPI AG
2021-06-01
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| Series: | Data |
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| Online Access: | https://www.mdpi.com/2306-5729/6/6/66 |
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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 | DOAJ |
| 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. |
| first_indexed | 2024-03-10T10:15:25Z |
| format | Article |
| id | doaj.art-3174425c0f354865a8bd8ec9a556e3a6 |
| institution | Directory Open Access Journal |
| issn | 2306-5729 |
| language | English |
| last_indexed | 2024-03-10T10:15:25Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Data |
| spelling | doaj.art-3174425c0f354865a8bd8ec9a556e3a62023-11-22T00:49:34ZengMDPI AGData2306-57292021-06-01666610.3390/data6060066The NCAR Airborne 94-GHz Cloud Radar: Calibration and Data ProcessingUlrike Romatschke0Michael Dixon1Peisang Tsai2Eric Loew3Jothiram Vivekanandan4Jonathan Emmett5Robert Rilling6Earth Observing Laboratory, National Center for Atmospheric Research (NCAR), Boulder, CO 80301, USAEarth Observing Laboratory, National Center for Atmospheric Research (NCAR), Boulder, CO 80301, USAEarth Observing Laboratory, National Center for Atmospheric Research (NCAR), Boulder, CO 80301, USAEarth Observing Laboratory, National Center for Atmospheric Research (NCAR), Boulder, CO 80301, USAEarth Observing Laboratory, National Center for Atmospheric Research (NCAR), Boulder, CO 80301, USAEarth Observing Laboratory, National Center for Atmospheric Research (NCAR), Boulder, CO 80301, USAEarth Observing Laboratory, National Center for Atmospheric Research (NCAR), Boulder, CO 80301, USAThe 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.https://www.mdpi.com/2306-5729/6/6/66radarcloud physicsreflectivityradial velocity |
| spellingShingle | 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 Data radar cloud physics reflectivity radial velocity |
| 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 |
| url | https://www.mdpi.com/2306-5729/6/6/66 |
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