Differences
This shows you the differences between two versions of the page.
| Both sides previous revision Previous revision | |||
| excursions:university_of_helsinki_research_stay [2026/08/19 20:31] – ayush | excursions:university_of_helsinki_research_stay [2026/09/16 06:21] (current) – ayush | ||
|---|---|---|---|
| Line 21: | Line 21: | ||
| During the research stay, the riming retrieval by Ockenfuß et al. (2025) was applied to W-band cloud radar observations during the winter months between January 2024 and February 2025, in order to detect (near) surface riming cases. Dr. Maximilian Maahn (University of Leipzig) provided VISSS level 2 data for these cases. In addition, data from the Pluuvio rain gauge located next to the VISSS was acquired from the Cloudnet database. By comparison of the gauge derived mass flux with the volume flux, the ensemble mean snow density was derived. For the calculation of the volume flux, a gamma function particle size distribution is assumed. The method of Brawn and Upton 2008 was implemented for the VISSS, which allows to estimate the most likely gamma parameters and moments from data. It was found that for the selected example cases, the derived snow density is often lower than predicted by the unrimed mass-size relation from Moisseev et al. (2017). This will subsequently lead to unphysical, negative rime mass fractions in the retrieval. In the next steps, we will focus on the underlying assumptions in the calculations. Especially, the aspect ratio and unrimed mass size relation deserve greater investigation. With the now implemented data processing, Fig. 3 in Tiira et al. (2016) can be recreated with VISSS data. Subsequently, | During the research stay, the riming retrieval by Ockenfuß et al. (2025) was applied to W-band cloud radar observations during the winter months between January 2024 and February 2025, in order to detect (near) surface riming cases. Dr. Maximilian Maahn (University of Leipzig) provided VISSS level 2 data for these cases. In addition, data from the Pluuvio rain gauge located next to the VISSS was acquired from the Cloudnet database. By comparison of the gauge derived mass flux with the volume flux, the ensemble mean snow density was derived. For the calculation of the volume flux, a gamma function particle size distribution is assumed. The method of Brawn and Upton 2008 was implemented for the VISSS, which allows to estimate the most likely gamma parameters and moments from data. It was found that for the selected example cases, the derived snow density is often lower than predicted by the unrimed mass-size relation from Moisseev et al. (2017). This will subsequently lead to unphysical, negative rime mass fractions in the retrieval. In the next steps, we will focus on the underlying assumptions in the calculations. Especially, the aspect ratio and unrimed mass size relation deserve greater investigation. With the now implemented data processing, Fig. 3 in Tiira et al. (2016) can be recreated with VISSS data. Subsequently, | ||
| - | <wc figure | + | <met figure |
| - | src="https:// | + | src="/ |
| width=" | width=" | ||
| - | | + | |
| title=" | title=" | ||
| - | caption=" | + | caption=" |
| - | caption-align=" | + | |
| - | click-action=" | + | |
| - | load-animation=" | + | |
| - | background=" | + | |
| - | <wc figure | + | <met figure |
| - | src="https:// | + | src="/ |
| width=" | width=" | ||
| - | | + | |
| title=" | title=" | ||
| - | caption=" | + | caption=" |
| - | caption-align=" | + | |
| - | click-action=" | + | |
| - | load-animation=" | + | |
| - | background=" | + | |
| **References: | **References: | ||
| - | * Brawn, D., and U. Graham, 2008: Estimation of an atmospheric gamma drop size distribution using disdrometer data. Atmospheric Research 87.1, 66–79, doi: 10.1016/ | + | * Brawn, D., and U. Graham, 2008: Estimation of an atmospheric gamma drop size distribution using disdrometer data. Atmospheric Research 87.1, 66–79, doi: [[https:// |
| - | * Kneifel, S., and D. Moisseev, 2020: Long-Term Statistics of Riming in Nonconvective Clouds Derived from Ground-Based Doppler Cloud Radar Observations. Journal of the Atmospheric Sciences 77.10, 3495–3508, | + | * Kneifel, S., and D. Moisseev, 2020: Long-Term Statistics of Riming in Nonconvective Clouds Derived from Ground-Based Doppler Cloud Radar Observations. Journal of the Atmospheric Sciences 77.10, 3495–3508, |
| - | * Moisseev, D., A. von Lerber, and J. Tiira, 2017: Quantifying the effect of riming on snowfall using ground-based observations. Journal of Geophysical Research: Atmospheres 122.7, 4019–4037, | + | * Moisseev, D., A. von Lerber, and J. Tiira, 2017: Quantifying the effect of riming on snowfall using ground-based observations. Journal of Geophysical Research: Atmospheres 122.7, 4019–4037, |
| - | * Ockenfuß, P., et al., 2025: Spatial and Temporal Scales of Riming Events in Nonconvective Clouds Derived From Long-Term Cloud Radar Observations in Germany. Journal of Geophysical Research: Atmospheres 130.4., doi: 10.1029/ | + | * Ockenfuß, P., et al., 2025: Spatial and Temporal Scales of Riming Events in Nonconvective Clouds Derived From Long-Term Cloud Radar Observations in Germany. Journal of Geophysical Research: Atmospheres 130.4., doi: [[https:// |
| - | * Tiira, J., et al., 2016: Ensemble mean density and its connection to other microphysical properties of falling snow as observed in Southern Finland. Atmospheric Measurement Techniques 9.9, 4825–4841, | + | * Tiira, J., et al., 2016: Ensemble mean density and its connection to other microphysical properties of falling snow as observed in Southern Finland. Atmospheric Measurement Techniques 9.9, 4825–4841, |
| </ | </ | ||