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| staging:corsipp [2026/09/24 19:42] – ayush | staging:corsipp [2026/09/24 19:52] (current) – ayush |
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| Wind and turbulence play an important role in orographically complex areas and lead to an increase in riming and secondary ice production (SIP, Ramelli et al., 2021). We analyzed wind and turbulence and found that many of our measurement days were strongly influenced by Gothic Mountain. Our measurement devices are located in the lee of the Gothic Mountain during westerly winds, which leads to an area of lee-induced flow disturbance (ALIFD), resulting in increased wind shear along the edges of the ALIFD (at about $500\,\mathrm{m}$ and $1000\,\mathrm{m}$ AGL). The two areas of increased wind shear can be clearly identified by the two maxima of eddy dissipation rate at $500\,\mathrm{m}$ and $1000\,\mathrm{m}$ AGL (Fig. 1). WSW-WNW is also the main wind direction for precipitation events with an amount $>0.5\,\mathrm{mm/h}$. These layers of turbulence, which were almost always present, complicate the further investigation for the determination of riming and SIP, as common retrievals used to detect these processes are not reliable in turbulent conditions. Another point we focused on was the investigation of different Specific Differential Phase ($K_\mathrm{DP}$) signals. Using the collocated in situ measurements from VISSS, we found that snow particle populations with different properties, sizes and number concentrations lead to similar $K_\mathrm{DP}$ magnitudes. This is shown in Figure 2 where averaged $K_\mathrm{DP}$ values close to the ground plotted against $D_{32}$ obtained from the VISSS (proxy for the mean mass-weighted diameter of the particle population) and the total number concentration $N_\mathrm{tot}$. Currently, we cannot rule out the contribution of bigger, low number concentration aggregates on W-band $K_\mathrm{DP}$ as particle populations with low $N_\mathrm{tot}$ and high $D_{32}$ produce similar $K_\mathrm{DP}$ values as populations with low $D_{32}$ and high $N_\mathrm{tot}$. Another interesting find was that blowing snow appears to be capable of producing high $K_\mathrm{DP}$ values as well. | Wind and turbulence play an important role in orographically complex areas and lead to an increase in riming and secondary ice production (SIP, Ramelli et al., 2021). We analyzed wind and turbulence and found that many of our measurement days were strongly influenced by Gothic Mountain. Our measurement devices are located in the lee of the Gothic Mountain during westerly winds, which leads to an area of lee-induced flow disturbance (ALIFD), resulting in increased wind shear along the edges of the ALIFD (at about $500\,\mathrm{m}$ and $1000\,\mathrm{m}$ AGL). The two areas of increased wind shear can be clearly identified by the two maxima of eddy dissipation rate at $500\,\mathrm{m}$ and $1000\,\mathrm{m}$ AGL (Fig. 1). WSW-WNW is also the main wind direction for precipitation events with an amount $>0.5\,\mathrm{mm/h}$. These layers of turbulence, which were almost always present, complicate the further investigation for the determination of riming and SIP, as common retrievals used to detect these processes are not reliable in turbulent conditions. Another point we focused on was the investigation of different Specific Differential Phase ($K_\mathrm{DP}$) signals. Using the collocated in situ measurements from VISSS, we found that snow particle populations with different properties, sizes and number concentrations lead to similar $K_\mathrm{DP}$ magnitudes. This is shown in Figure 2 where averaged $K_\mathrm{DP}$ values close to the ground plotted against $D_{32}$ obtained from the VISSS (proxy for the mean mass-weighted diameter of the particle population) and the total number concentration $N_\mathrm{tot}$. Currently, we cannot rule out the contribution of bigger, low number concentration aggregates on W-band $K_\mathrm{DP}$ as particle populations with low $N_\mathrm{tot}$ and high $D_{32}$ produce similar $K_\mathrm{DP}$ values as populations with low $D_{32}$ and high $N_\mathrm{tot}$. Another interesting find was that blowing snow appears to be capable of producing high $K_\mathrm{DP}$ values as well. |
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| | /* |
| | <met figure-grid |
| | columns="2" |
| | width="1200" |
| | figure-defaults='{"variant":"outline"}'> |
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| | <met figure |
| | src="https://www2.meteo.uni-bonn.de/spp2115/lib/exe/fetch.php?media=projects:corsipp_2024_1.png" |
| | title="Figure 1" |
| | caption="Eddy dissipation rate with height, processed by Teresa Vogl from KAZR MDV (Vogl et al., 2022)"> |
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| | <met figure |
| | src="https://www2.meteo.uni-bonn.de/spp2115/lib/exe/fetch.php?media=projects:corsipp_2024_2.png" |
| | title="Figure 2" |
| | caption="Scatterplot of LIMRAD94 $K_\mathrm{DP}$ vs. VISSS number concentration for DJF 2022/23. The $y$-scale is logarithmic. LIMRAD94 $K_\mathrm{DP}$ was spatially averaged between $100$ and $500\,\mathrm{m}$ above ground and temporally averaged to fit the VISSS time resolution of one minute. Colors show the mass weighted mean diameter ($D_{32}$) as described in Maahn et al., (2024a). A total of 11,164 data points (i.e. 11,164 minutes) was used for the plot."> |
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| | </met> |
| | */ |
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| <met figure | <met figure |
| title="Figure 2" | title="Figure 2" |
| caption="Scatterplot of LIMRAD94 $K_\mathrm{DP}$ vs. VISSS number concentration for DJF 2022/23. The $y$-scale is logarithmic. LIMRAD94 $K_\mathrm{DP}$ was spatially averaged between $100$ and $500\,\mathrm{m}$ above ground and temporally averaged to fit the VISSS time resolution of one minute. Colors show the mass weighted mean diameter ($D_{32}$) as described in Maahn et al., (2024a). A total of 11,164 data points (i.e. 11,164 minutes) was used for the plot."> | caption="Scatterplot of LIMRAD94 $K_\mathrm{DP}$ vs. VISSS number concentration for DJF 2022/23. The $y$-scale is logarithmic. LIMRAD94 $K_\mathrm{DP}$ was spatially averaged between $100$ and $500\,\mathrm{m}$ above ground and temporally averaged to fit the VISSS time resolution of one minute. Colors show the mass weighted mean diameter ($D_{32}$) as described in Maahn et al., (2024a). A total of 11,164 data points (i.e. 11,164 minutes) was used for the plot."> |
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| ===Analysis of VISSS In Situ Data=== | ===Analysis of VISSS In Situ Data=== |
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| The next step is to compare the times with a high occurrence of one particle shape (e.g. only needles, only graupel, only dendrites, only aggregates) with the measured radar variables in order to detect differences in the radar variables depending on the particle shape. | The next step is to compare the times with a high occurrence of one particle shape (e.g. only needles, only graupel, only dendrites, only aggregates) with the measured radar variables in order to detect differences in the radar variables depending on the particle shape. |
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| | /* |
| | <met figure-grid |
| | columns="2" |
| | width="1200" |
| | figure-defaults='{"variant":"outline"}'> |
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| | <met figure |
| | src="https://www2.meteo.uni-bonn.de/spp2115/lib/exe/fetch.php?media=projects:corsipp_2024_3.png" |
| | title="Figure 3" |
| | caption="Distribution of particle shapes at Gothic in winter 2022/2023 (December, January, and February)."> |
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| | <met figure |
| | src="https://www2.meteo.uni-bonn.de/spp2115/lib/exe/fetch.php?media=projects:corsipp_2024_4.png" |
| | title="Figure 4" |
| | caption="Frequency of degree of riming."> |
| | </met> |
| | */ |
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| <met figure | <met figure |