Statistical Relations among Solid Precipitation, Atmospheric Moisture and Cloud Parameters in the Arctic
Observations collected during cold-season precipitation periods at Utquagvik, Alaska and at the multidisciplinary drifting observatory for the study of Arctic climate (MOSAiC) are used to statistically analyze the relations among the atmospheric water cycle parameters including the columnar supercoo...
| Autore principale: | |
|---|---|
| Natura: | Articolo |
| Lingua: | English |
| Pubblicazione: |
MDPI AG
2024-01-01
|
| Serie: | Atmosphere |
| Soggetti: | |
| Accesso online: | https://www.mdpi.com/2073-4433/15/1/132 |
| _version_ | 1827372793533038592 |
|---|---|
| author | Sergey Y. Matrosov |
| author_facet | Sergey Y. Matrosov |
| author_sort | Sergey Y. Matrosov |
| collection | DOAJ |
| description | Observations collected during cold-season precipitation periods at Utquagvik, Alaska and at the multidisciplinary drifting observatory for the study of Arctic climate (MOSAiC) are used to statistically analyze the relations among the atmospheric water cycle parameters including the columnar supercooled liquid and ice amounts (expressed as liquid-water and ice-water paths, i.e., LWP and IWP), the integrated water vapor (IWV) and the near-surface snowfall rate. Data come from radar and radiometer-based retrievals and from optical precipitation sensors. While the correlation between snowfall rate and LWP is rather weak, correlation coefficients between radar-derived snowfall rate and IWP are high (~0.8), which is explained, in part, by the generally low LWP/IWP ratios during significant precipitation. Correlation coefficients between snowfall rate and IWV are moderate (~0.45). Correlations are generally weaker if snowfall is estimated by optical sensors, which is, in part, due to blowing snow. Correlation coefficients between near-surface temperature and snowfall rates are low (<i>r</i> < 0.3). The results from the Alaska and MOSAiC sites are generally similar. These results are not very sensitive to the amount of time averaging (e.g., 15 min averaging versus daily averages). Observationally based relations among the water cycle parameters are informative about atmospheric moisture conversion processes and can be used for model evaluations. |
| first_indexed | 2024-03-08T11:06:32Z |
| format | Article |
| id | doaj.art-9c7c736b76444f008ace3d44cdd4b6d3 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-08T11:06:32Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-9c7c736b76444f008ace3d44cdd4b6d32024-01-26T15:02:38ZengMDPI AGAtmosphere2073-44332024-01-0115113210.3390/atmos15010132Statistical Relations among Solid Precipitation, Atmospheric Moisture and Cloud Parameters in the ArcticSergey Y. Matrosov0Cooperative Institute for Research in Environmental Sciences, University of Colorado and NOAA Physical Sciences Laboratory, Boulder, CO 80309, USAObservations collected during cold-season precipitation periods at Utquagvik, Alaska and at the multidisciplinary drifting observatory for the study of Arctic climate (MOSAiC) are used to statistically analyze the relations among the atmospheric water cycle parameters including the columnar supercooled liquid and ice amounts (expressed as liquid-water and ice-water paths, i.e., LWP and IWP), the integrated water vapor (IWV) and the near-surface snowfall rate. Data come from radar and radiometer-based retrievals and from optical precipitation sensors. While the correlation between snowfall rate and LWP is rather weak, correlation coefficients between radar-derived snowfall rate and IWP are high (~0.8), which is explained, in part, by the generally low LWP/IWP ratios during significant precipitation. Correlation coefficients between snowfall rate and IWV are moderate (~0.45). Correlations are generally weaker if snowfall is estimated by optical sensors, which is, in part, due to blowing snow. Correlation coefficients between near-surface temperature and snowfall rates are low (<i>r</i> < 0.3). The results from the Alaska and MOSAiC sites are generally similar. These results are not very sensitive to the amount of time averaging (e.g., 15 min averaging versus daily averages). Observationally based relations among the water cycle parameters are informative about atmospheric moisture conversion processes and can be used for model evaluations.https://www.mdpi.com/2073-4433/15/1/132snowfallcloud contentwater vaporArctic climatewater cycleatmospheric moisture conversion |
| spellingShingle | Sergey Y. Matrosov Statistical Relations among Solid Precipitation, Atmospheric Moisture and Cloud Parameters in the Arctic Atmosphere snowfall cloud content water vapor Arctic climate water cycle atmospheric moisture conversion |
| title | Statistical Relations among Solid Precipitation, Atmospheric Moisture and Cloud Parameters in the Arctic |
| title_full | Statistical Relations among Solid Precipitation, Atmospheric Moisture and Cloud Parameters in the Arctic |
| title_fullStr | Statistical Relations among Solid Precipitation, Atmospheric Moisture and Cloud Parameters in the Arctic |
| title_full_unstemmed | Statistical Relations among Solid Precipitation, Atmospheric Moisture and Cloud Parameters in the Arctic |
| title_short | Statistical Relations among Solid Precipitation, Atmospheric Moisture and Cloud Parameters in the Arctic |
| title_sort | statistical relations among solid precipitation atmospheric moisture and cloud parameters in the arctic |
| topic | snowfall cloud content water vapor Arctic climate water cycle atmospheric moisture conversion |
| url | https://www.mdpi.com/2073-4433/15/1/132 |
| work_keys_str_mv | AT sergeyymatrosov statisticalrelationsamongsolidprecipitationatmosphericmoistureandcloudparametersinthearctic |