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| projects:picnicc [2026/10/06 16:04] – ayush | projects:picnicc [2026/10/06 16:08] (current) – ayush |
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| == Contribution of TROPOS == | == Contribution of TROPOS == |
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| A new algorithm was implemented to determine the Vertical Distribution of Particle Shape (VDPS) in a cloud, using Range Height Indicator (RHI) scans from $90^\circ$ (zenith pointing) to $150^\circ$ elevation angle of the Slanted Linear Depolarization Ratio (SLDR) and the cross-correlation coefficient ($\rho_\mathrm{cx}$),shown in Figure 1(b) and 1(c), which are sensible to the shape and orientation of particles, respectively (Myagkov et al., 2016). From the elevation dependency of these two parameters and assuming that the hydrometeors are oriented horizontally along their long axis, the microphysical parameter polarizability ratio (i.e., density-weighted aspect ratio) can be derived as a function of height. A polarizability ratio $\xi = 1$ describes isometric particles (illustrated in Figure 1(e) with the dashed red line), while $\xi \lt 1$ and $\xi \gt 1$ describe oblate and prolate particles, respectively. The particularity of this new approach is the combination of a spheroid scattering model with polarimetric measurements. The new method was applied to data from two field campaigns (CyCARE in Limassol, Cyprus, and DACAPO-PESO in Punta Arenas, Chile, Radenz et al., 2021) and is prepared to run automatically. This vertical distribution of the particle shape in a cloud is a new way to understand processes like riming and aggregation, as either one of these processes in general contribute to a vertical change of microphysical properties. | A new algorithm was implemented to determine the Vertical Distribution of Particle Shape (VDPS) in a cloud, using Range Height Indicator (RHI) scans from $90^\circ$ (zenith pointing) to $150^\circ$ elevation angle of the Slanted Linear Depolarization Ratio (SLDR) and the cross-correlation coefficient ($\rho_\mathrm{cx}$),shown in Figure $1\text{(b)}$ and $1\text{(c)}$, which are sensible to the shape and orientation of particles, respectively (Myagkov et al., 2016). From the elevation dependency of these two parameters and assuming that the hydrometeors are oriented horizontally along their long axis, the microphysical parameter polarizability ratio (i.e., density-weighted aspect ratio) can be derived as a function of height. A polarizability ratio $\xi = 1$ describes isometric particles (illustrated in Figure $1\text{(e)}$ with the dashed red line), while $\xi \lt 1$ and $\xi \gt 1$ describe oblate and prolate particles, respectively. The particularity of this new approach is the combination of a spheroid scattering model with polarimetric measurements. The new method was applied to data from two field campaigns (CyCARE in Limassol, Cyprus, and DACAPO-PESO in Punta Arenas, Chile, Radenz et al., 2021) and is prepared to run automatically. This vertical distribution of the particle shape in a cloud is a new way to understand processes like riming and aggregation, as either one of these processes in general contribute to a vertical change of microphysical properties. |
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| == Contribution of University of Leipzig == | == Contribution of University of Leipzig == |
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| Figure 1(a) shows the time-height plot of reflectivity measured by the W-Band radar in Punta Arenas between 1 :30 and 5:00 UTC. A high ice cloud merges with mid-level layer clouds, leading to precipitation (rain) between 2:45 and 4:30 UTC. The ANN-based riming retrieval (Figure 1(d)) predicts riming for the height range between approximately $3.5$ and $5 \mathrm{km}$, between 2:45 and 3:30 UTC. The height spectrogram of cloud radar Doppler spectra for 3:30 UTC (Figure 1(f)) reveals that liquid water is present up to an altitude of $5 \mathrm{km}$, corroborating the prediction of the ANN. In Figure 1(b) and 1%%(c)%%, RHI-scans of SLDR and $\rho_\mathrm{cx}$ are performed with a SLDR-mode scanning cloud radar used during the campaign and show a vertical stratification of the cloud where different microphysical processes can occur. The vertical distribution of the polarizability ratio (Figure 1(e)), illustrates a stable layer of oblate particles from $5$ to $8 \mathrm{km}$ ($\xi \lt 1$), where particles precipitate into the riming layer detected by ANN between $3.8$ and $5 \mathrm{km}$, due to the presence of supercooled liquid droplets, and becomes more and more spherical producing graupel particles from $2.5 \mathrm{km}$ to $3.8 \mathrm{km}$ ($\xi = 1$). The ANN is able to detect riming while the VDPS method allows us to observe graupel particles producing by riming processes. The combination of these two methods add important information for the differentiation of riming and aggregation processes. | Figure $1\text{(a)}$ shows the time-height plot of reflectivity measured by the W-Band radar in Punta Arenas between 1 :30 and 5:00 UTC. A high ice cloud merges with mid-level layer clouds, leading to precipitation (rain) between 2:45 and 4:30 UTC. The ANN-based riming retrieval (Figure $1\text{(d)}$) predicts riming for the height range between approximately $3.5$ and $5 \mathrm{km}$, between 2:45 and 3:30 UTC. The height spectrogram of cloud radar Doppler spectra for 3:30 UTC (Figure $1\text{(f)}$) reveals that liquid water is present up to an altitude of $5 \mathrm{km}$, corroborating the prediction of the ANN. In Figure $1\text{(b)}$ and $1\text{(c)}$, RHI-scans of SLDR and $\rho_\mathrm{cx}$ are performed with a SLDR-mode scanning cloud radar used during the campaign and show a vertical stratification of the cloud where different microphysical processes can occur. The vertical distribution of the polarizability ratio (Figure $1\text{(e)}$), illustrates a stable layer of oblate particles from $5$ to $8 \mathrm{km}$ ($\xi \lt 1$), where particles precipitate into the riming layer detected by ANN between $3.8$ and $5 \mathrm{km}$, due to the presence of supercooled liquid droplets, and becomes more and more spherical producing graupel particles from $2.5 \mathrm{km}$ to $3.8 \mathrm{km}$ ($\xi = 1$). The ANN is able to detect riming while the VDPS method allows us to observe graupel particles producing by riming processes. The combination of these two methods add important information for the differentiation of riming and aggregation processes. |
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| == References == | == References == |