Arctic Weather Satellite Sensitivity to Supercooled Liquid Water in Snowfall Conditions
The aim of this study is to highlight the issue of missed supercooled liquid water (SLW) detection in the current radar/lidar derived products and to investigate the potential of the combined use of the EarthCARE mission and the Arctic Weather Satellite (AWS)—Microwave Radiometer (MWR) observations...
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MDPI AG
2024-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/22/4164 |
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| author | Andrea Camplani Paolo Sanò Daniele Casella Giulia Panegrossi Alessandro Battaglia |
| author_facet | Andrea Camplani Paolo Sanò Daniele Casella Giulia Panegrossi Alessandro Battaglia |
| author_sort | Andrea Camplani |
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| description | The aim of this study is to highlight the issue of missed supercooled liquid water (SLW) detection in the current radar/lidar derived products and to investigate the potential of the combined use of the EarthCARE mission and the Arctic Weather Satellite (AWS)—Microwave Radiometer (MWR) observations to fill this observational gap and to improve snowfall retrieval capabilities. The presence of SLW layers, which is typical of snowing clouds at high latitudes, represents a significant challenge for snowfall retrieval based on passive microwave (PMW) observations. The strong emission effect of SLW has the potential to mask the snowflake scattering signal in the high-frequency channels (>90 GHz) exploited for snowfall retrieval, while the detection capability of the combined radar/lidar SLW product—which is currently used as reference for the PMW-based snowfall retrieval algorithm—is limited to the cloud top due to SLW signal attenuation. In this context, EarthCARE, which is equipped with both a radar and a lidar, and the AWS-MWR, whose channels cover a range from 50 GHz to 325.15 GHz, offer a unique opportunity to improve both SLW detection and snowfall retrieval. In the current study, a case study is analyzed by comparing available PMW observations with AWS-MWR simulated signals for different scenarios of SLW layers, and an extensive comparison of the CloudSat brightness temperature (TB) product with the corresponding simulated signal is carried out. Simulated TBs are obtained from a radiative transfer model applied to cloud and precipitation profiles derived from the algorithm developed for the EarthCARE mission (CAPTIVATE). Different single scattering models are considered. This analysis highlights the missed detection of SLW layers embedded by the radar/lidar product and the sensitivity of AWS-MWR channels to SLW. Moreover, the new AWS 325.15 GHz channels are very sensitive to snowflakes in the atmosphere, and unaffected by SLW. Therefore, their combination with EarthCARE radar/lidar measurements can be exploited to both improve snowfall retrieval capabilities and to constrain snowfall microphysical properties. |
| format | Article |
| id | doaj-art-0bfde672f482420b9f83c0908a468cc1 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-0bfde672f482420b9f83c0908a468cc12025-08-20T02:27:38ZengMDPI AGRemote Sensing2072-42922024-11-011622416410.3390/rs16224164Arctic Weather Satellite Sensitivity to Supercooled Liquid Water in Snowfall ConditionsAndrea Camplani0Paolo Sanò1Daniele Casella2Giulia Panegrossi3Alessandro Battaglia4National Research Council of Italy, Institute of Atmospheric Sciences and Climate (CNR-ISAC), 00133 Rome, ItalyNational Research Council of Italy, Institute of Atmospheric Sciences and Climate (CNR-ISAC), 00133 Rome, ItalyNational Research Council of Italy, Institute of Atmospheric Sciences and Climate (CNR-ISAC), 00133 Rome, ItalyNational Research Council of Italy, Institute of Atmospheric Sciences and Climate (CNR-ISAC), 00133 Rome, ItalyNational Centre for Earth Observation, University of Leicester, Leicester LE1 7RH, UKThe aim of this study is to highlight the issue of missed supercooled liquid water (SLW) detection in the current radar/lidar derived products and to investigate the potential of the combined use of the EarthCARE mission and the Arctic Weather Satellite (AWS)—Microwave Radiometer (MWR) observations to fill this observational gap and to improve snowfall retrieval capabilities. The presence of SLW layers, which is typical of snowing clouds at high latitudes, represents a significant challenge for snowfall retrieval based on passive microwave (PMW) observations. The strong emission effect of SLW has the potential to mask the snowflake scattering signal in the high-frequency channels (>90 GHz) exploited for snowfall retrieval, while the detection capability of the combined radar/lidar SLW product—which is currently used as reference for the PMW-based snowfall retrieval algorithm—is limited to the cloud top due to SLW signal attenuation. In this context, EarthCARE, which is equipped with both a radar and a lidar, and the AWS-MWR, whose channels cover a range from 50 GHz to 325.15 GHz, offer a unique opportunity to improve both SLW detection and snowfall retrieval. In the current study, a case study is analyzed by comparing available PMW observations with AWS-MWR simulated signals for different scenarios of SLW layers, and an extensive comparison of the CloudSat brightness temperature (TB) product with the corresponding simulated signal is carried out. Simulated TBs are obtained from a radiative transfer model applied to cloud and precipitation profiles derived from the algorithm developed for the EarthCARE mission (CAPTIVATE). Different single scattering models are considered. This analysis highlights the missed detection of SLW layers embedded by the radar/lidar product and the sensitivity of AWS-MWR channels to SLW. Moreover, the new AWS 325.15 GHz channels are very sensitive to snowflakes in the atmosphere, and unaffected by SLW. Therefore, their combination with EarthCARE radar/lidar measurements can be exploited to both improve snowfall retrieval capabilities and to constrain snowfall microphysical properties.https://www.mdpi.com/2072-4292/16/22/4164AWSsupercooled liquid watermicrowave remote sensingsnowfall retrieval |
| spellingShingle | Andrea Camplani Paolo Sanò Daniele Casella Giulia Panegrossi Alessandro Battaglia Arctic Weather Satellite Sensitivity to Supercooled Liquid Water in Snowfall Conditions Remote Sensing AWS supercooled liquid water microwave remote sensing snowfall retrieval |
| title | Arctic Weather Satellite Sensitivity to Supercooled Liquid Water in Snowfall Conditions |
| title_full | Arctic Weather Satellite Sensitivity to Supercooled Liquid Water in Snowfall Conditions |
| title_fullStr | Arctic Weather Satellite Sensitivity to Supercooled Liquid Water in Snowfall Conditions |
| title_full_unstemmed | Arctic Weather Satellite Sensitivity to Supercooled Liquid Water in Snowfall Conditions |
| title_short | Arctic Weather Satellite Sensitivity to Supercooled Liquid Water in Snowfall Conditions |
| title_sort | arctic weather satellite sensitivity to supercooled liquid water in snowfall conditions |
| topic | AWS supercooled liquid water microwave remote sensing snowfall retrieval |
| url | https://www.mdpi.com/2072-4292/16/22/4164 |
| work_keys_str_mv | AT andreacamplani arcticweathersatellitesensitivitytosupercooledliquidwaterinsnowfallconditions AT paolosano arcticweathersatellitesensitivitytosupercooledliquidwaterinsnowfallconditions AT danielecasella arcticweathersatellitesensitivitytosupercooledliquidwaterinsnowfallconditions AT giuliapanegrossi arcticweathersatellitesensitivitytosupercooledliquidwaterinsnowfallconditions AT alessandrobattaglia arcticweathersatellitesensitivitytosupercooledliquidwaterinsnowfallconditions |