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 Unlike the T-matrix, DDA (Discrete Dipole Approximation, Yurkin and Hoekstra, 2007) is an advanced scattering model that can simulate scattering properties from irregularly shaped particles. This is obtained by representing the complex shapes of particles with a set of cubic voxels distributed in space. By increasing the resolution of such representation (i.e. reducing the size length of the cubes), it is possible to describe the complex geometrical details of small ice crystals. Unlike the T-matrix, DDA (Discrete Dipole Approximation, Yurkin and Hoekstra, 2007) is an advanced scattering model that can simulate scattering properties from irregularly shaped particles. This is obtained by representing the complex shapes of particles with a set of cubic voxels distributed in space. By increasing the resolution of such representation (i.e. reducing the size length of the cubes), it is possible to describe the complex geometrical details of small ice crystals.
 \\ \\ \\ \\
-Firstly, we simulated realistically shaped cloud ice particles (i.e., single crystals, monomers) including plates, simply defined by their geometrical properties and representing crystals up to sizes for 0.5 mm, and dendrite shapes using Reiter's algorithm (Reiter, 2005), representing larger crystals. Figure  1 shows a few examples of simulated realistic dendrites and plates.+Firstly, we simulated realistically shaped cloud ice particles (i.e., single crystals, monomers) including plates, simply defined by their geometrical properties and representing crystals up to sizes for $0.5\,\mathrm{mm}$, and dendrite shapes using Reiter's algorithm (Reiter, 2005), representing larger crystals. Figure 1 shows a few examples of simulated realistic dendrites and plates.
  
-<html> +<met figure-grid 
-<style> +    images='[ 
-    .image-container { +        {"src":"https://www2.meteo.uni-bonn.de/spp2115/lib/exe/fetch.php?media=projects:pristine_dendrite_1.png","alt":"Dendrite 1"}, 
-        display: flex; +        {"src":"https://www2.meteo.uni-bonn.de/spp2115/lib/exe/fetch.php?media=projects:pristine_dendrite_2.png","alt":"Dendrite 2"}, 
-        flex-wrap: wrap; /* Wrap on small screens */ +        {"src":"https://www2.meteo.uni-bonn.de/spp2115/lib/exe/fetch.php?media=projects:pristine_dendrite_3.png","alt":"Dendrite 3"}, 
-        justify-content: center; +        {"src":"https://www2.meteo.uni-bonn.de/spp2115/lib/exe/fetch.php?media=projects:pristine_dendrite_4.png","alt":"Dendrite 4"}, 
-        gap: 10px; /* Space between images */ +        {"src":"https://www2.meteo.uni-bonn.de/spp2115/lib/exe/fetch.php?media=projects:pristine_dendrite_5.png","alt":"Dendrite 5"}, 
-        margin: 20px auto; /* Center image container */ +        {"src":"https://www2.meteo.uni-bonn.de/spp2115/lib/exe/fetch.php?media=projects:pristine_dendrite_6.png","alt":"Dendrite 6"} 
-        max-width: 60vw; +    ]' 
-        padding: 10px; +    title="Figure 1" 
-        border-radius: 5px; +    caption="Simulated dendrites and plates." 
-        box-sizing: border-box; /* Ensure padding and border are included in the element's total width and height */ +    caption-align="center" 
-    } +    layout="row" 
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-        } +
-    } +
-</style>+
  
 +As the focus is on the consistency between the microphysical assumptions made in the ICON model and the forward simulations, we determine the aspect ratio of the simulated monomers to match the ICON mass-size relationship. Figure 2 shows the resulting aspect ratios over particle size for different shapes of dendrites. With increasing size, aspect ratio, in general, decreases. The large variability of aspect ratio at lower sizes is caused by using a constant resolution ($20\,\mathrm{µm}$) for all the particles. However, we are now revisiting our strategy and adapting the resolution to homogenize the representativity of particle shapes at various size ranges.
  
-<div class="image-container"> +<met figure 
-    <img src="https://www2.meteo.uni-bonn.de/spp2115/lib/exe/fetch.php?media=projects:pristine_dendrite_1.png" alt="Dendrite 1"> +    src="https://www2.meteo.uni-bonn.de/spp2115/lib/exe/fetch.php?media=projects:pristine_fig_2.png" 
-    <img src="https://www2.meteo.uni-bonn.de/spp2115/lib/exe/fetch.php?media=projects:pristine_dendrite_2.png" alt="Dendrite 2"> +    width="800" 
-    <img src="https://www2.meteo.uni-bonn.de/spp2115/lib/exe/fetch.php?media=projects:pristine_dendrite_3.png" alt="Dendrite 3"> +    fit="responsive" 
-    <img src="https://www2.meteo.uni-bonn.de/spp2115/lib/exe/fetch.php?media=projects:pristine_dendrite_4.png" alt="Dendrite 4"> +    title="Figure 2" 
-    <img src="https://www2.meteo.uni-bonn.de/spp2115/lib/exe/fetch.php?media=projects:pristine_dendrite_5.png" alt="Dendrite 5"> +    caption="Variation of geometrical aspect respect ratio with size for different dendrite shapes." 
-    <img src="https://www2.meteo.uni-bonn.de/spp2115/lib/exe/fetch.php?media=projects:pristine_dendrite_6.png" alt="Dendrite 6"> +    caption-align="left" 
-</div> +    load-animation="zoom-in" 
-</html> +    background="white" 
-<TEXT align="center">**Figure 1:** Simulated dendrites and plates.</TEXT> +    zoomable lightbox>
-\\+
  
-As the focus is on the consistency between the microphysical assumptions made in the ICON model and the forward simulations, we determine the aspect ratio of the simulated monomers to match the ICON mass-size relationship. Figure 2 shows the resulting aspect ratios over particle size for different shapes of dendrites. With increasing size, aspect ratio, in general, decreases. The large variability of aspect ratio at lower sizes is caused by using a constant resolution (20um) for all the particles. However, we are now revisiting our strategy and adapting the resolution to homogenize the representativity of particle shapes at various size ranges. +Further, we use an aggregation model to generate aggregates using the simulated plates and dendrites. Figure 3 shows the mass-size relation for the generated aggregates and the ICON 2mom snow microphysics. Since snow aggregation is a stochastic process, it is not straightforward to control the size and mass of the resulting aggregate as it was for the individual crystals. For this reason, we generated a lot of aggregate shapes varying the parameters of the aggregation process (namely, the number and size distribution of the colliding crystals that end up composing the aggregate). For the smallest aggregate size, the icon assumptions require a far too dense aggregate shape that wasn’t possible to model using realistic assumptions. For this portion of the size spectrum we plan to use again single crystals. For the larger sizes, we get some aggregates that fall on the ICON 2moment snow mass-size relation. We select those aggregates that best fit the ICON 2-moment mass-size relation to proceed further with the scattering calculation. The ICON size limit for the snow class is $0.05\,\mathrm{mm}$ to $50\,\mathrm{mm}$. We divided the size range into 256 linear-size bins and simulated snow aggregates up to $13\,\mathrm{mm}$ so far. The selection criteria first consisted of in a mass-threshold that discarded all shapes with masses that were more than 10% away from the assumed relation. Then we selected for each size bin the shape that had it maximum dimension closest to the bin center.
-\\ \\ +
-<WRAP centeralign> +
-{{ projects:pristine_fig_2.png?direct&800&nolink }} +
-</WRAP> +
-\\ +
-<WRAP tablewidth 60% center>**Figure 2:** Variation of geometrical aspect respect ratio with size for different dendrite shapes.</WRAP>+
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 +DDA considers each cube representing the scattering particle as a discrete polarizable element (originally assumed to be an electric dipole, hence the name DDA). The model simulates the interaction of all these polarizable elements with an incident electromagnetic wave and among all the elements. The superposition of the resulting radiated electric field allows to calculate the scattering properties of the particle. We used DDA to compute the forward and backward scattering properties for each of the selected ice and snow particles for multi-frequency radar bands. Further, we calculate the azimuthal orientation average for each particle from the DDA simulated scattering properties and provide the data to DWD for implementation in EMVORADO. The azimuthal orientation average for the C-band was calculated for the first result. while the scattering calculations for the X, Ka and W bands are currently running.
 +
 +<met figure
 +    src="https://www2.meteo.uni-bonn.de/spp2115/lib/exe/fetch.php?media=projects:pristine_fig_3.png"
 +    width="800"
 +    fit="responsive"
 +    title="Figure 3"
 +    caption="Green line represents the mass-size relation of snow in the ICON 2-moment microphysics. Blue dots represent masses of the simulated aggregates, and the yellow dots highlight those aggregate masses within 10% of the ICON snow mass."
 +    caption-align="left"
 +    load-animation="zoom-in"
 +    background="white"
 +    zoomable lightbox>
  
-Further, we use an aggregation model to generate aggregates using the simulated plates and dendrites. Figure 3 shows the mass-size relation for the generated aggregates and the ICON 2mom snow microphysics. Since snow aggregation is a stochastic process, it is not straightforward to control the size and mass of the resulting aggregate as it was for the individual crystals. For this reason, we generated a lot of aggregate shapes varying the parameters of the aggregation process (namely, the number and size distribution of the colliding crystals that end up composing the aggregate). For the smallest aggregate size, the icon assumptions require a far too dense aggregate shape that wasn’t possible to model using realistic assumptions. For this portion of the size spectrum we plan to use again single crystals. For the larger sizes, we get some aggregates that fall on the ICON 2moment snow mass-size relation. We select those aggregates that best fit the ICON 2-moment mass-size relation to proceed further with the scattering calculation. The ICON size limit for the snow class is 0.05 mm to 50 mm. We divided the size range into 256 linear-size bins and  simulated snow aggregates up to 13 mm so far. The selection criteria first consisted of in a mass-threshold that discarded all shapes with masses that were more than 10% away from the assumed relation. Then we selected for each size bin the shape that had it maximum dimension closest to the bin center. 
-\\ \\ 
-DDA considers each cube representing the scattering particle as a discrete polarizable element (originally assumed to be an electric dipole, hence the name DDA). The model simulates the interaction of all these polarizable elements with an incident electromagnetic wave and among all the elements. The superposition of the resulting radiated electric field allows to calculate the scattering properties of the particle. We used DDA to compute the forward and backward scattering properties for each of the selected ice and snow particles for multi-frequency radar bands. Further, we calculate the azimuthal orientation average for each particle from the DDA simulated scattering properties and provide the data to DWD for implementation in EMVORADO. The azimuthal orientation average for the C-band was calculated for the first result. while the scattering calculations for the X, Ka and W bands are currently running. 
-\\ \\ 
-<WRAP centeralign> 
-{{ projects:pristine_fig_3.png?direct&800&nolink }} 
-</WRAP> 
-\\ 
-<WRAP tablewidth 60% center>**Figure 3:** Green line represents the mass-size relation of snow in the ICON 2-moment microphysics. Blue dots represent masses of the simulated aggregates, and the yellow dots highlight those aggregate masses within 10% of the ICON snow mass.</WRAP> 
-\\ \\ 
 We plan to use McSnow, a Lagrangian particle-based model (Brdar and Seifert, 2018) that can help us get more realistic ice particles. We can get more realistic snow shapes in conjunction with the aggregation model that we used before. Getting particle structures by using a combination of a Lagrangian super-particle model and an aggregation model rather than from empirical and idealized habits will allow an evaluation of the uncertainties of the computed scattering properties that arise from the variety of snow particle shapes, which are unknown in operational microphysical schemes. As our focus is to keep consistency with microphysical assumptions made in the ICON model, we are currently evaluating the discrepancies of ice and snow microphysical properties between McSnow and ICON when simulating the same cloud scene. Also, the DDA scattering calculations with varying resolution particles are ongoing. We plan to use McSnow, a Lagrangian particle-based model (Brdar and Seifert, 2018) that can help us get more realistic ice particles. We can get more realistic snow shapes in conjunction with the aggregation model that we used before. Getting particle structures by using a combination of a Lagrangian super-particle model and an aggregation model rather than from empirical and idealized habits will allow an evaluation of the uncertainties of the computed scattering properties that arise from the variety of snow particle shapes, which are unknown in operational microphysical schemes. As our focus is to keep consistency with microphysical assumptions made in the ICON model, we are currently evaluating the discrepancies of ice and snow microphysical properties between McSnow and ICON when simulating the same cloud scene. Also, the DDA scattering calculations with varying resolution particles are ongoing.
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 Based on the ready-to-use data of azimuthally-averaged polarimetric scattering properties of individual hydrometeors provided by the University of Cologne, we produce EMVORADO-equivalent polarimetric bulk scattering lookup tables. In this first stage, we apply external, python-based tools to perform the integrations over the hydrometeor-class specific particle size (or mass) distributions, consistently with ICON microphysics, and particle orientation distributions. These lookup tables are then used by EMVORADO in the same way as the ones internally derived from Mie or T-matrix calculations. In a later step, the size and orientation integration procedures will be performed within EMVORADO consistently with those of the other scattering methods, and the lookup tables will be produced internally from externally calculated single particle scattering properties like those produced by the University of Cologne. Based on the ready-to-use data of azimuthally-averaged polarimetric scattering properties of individual hydrometeors provided by the University of Cologne, we produce EMVORADO-equivalent polarimetric bulk scattering lookup tables. In this first stage, we apply external, python-based tools to perform the integrations over the hydrometeor-class specific particle size (or mass) distributions, consistently with ICON microphysics, and particle orientation distributions. These lookup tables are then used by EMVORADO in the same way as the ones internally derived from Mie or T-matrix calculations. In a later step, the size and orientation integration procedures will be performed within EMVORADO consistently with those of the other scattering methods, and the lookup tables will be produced internally from externally calculated single particle scattering properties like those produced by the University of Cologne.
 +
 +<met figure
 +    src="https://www2.meteo.uni-bonn.de/spp2115/lib/exe/fetch.php?media=projects:pristine_fig_4.png"
 +    width="800"
 +    fit="responsive"
 +    title="Figure 4"
 +    caption="Equivalent $Z_\mathrm{H}$, $Z_\mathrm{DR}$, and $K_\mathrm{DP}$ from EMVORADO cloud ice bulk scattering lookup tables from operational internal T-matrix calculations and external DDA-based scattering data from the PRISTINE project. Note that at this state, data for small particles ($< 0.5\,\mathrm{mm}$) is lacking, hence results for small $D_\mathrm{mean}$ should be taken with care."
 +    caption-align="left"
 +    load-animation="zoom-in"
 +    background="white"
 +    zoomable lightbox>
 +
 +So far, lookup tables have been produced for cloud ice, i.e. from single crystals scattering properties, illustrated in Figure 4 in the form of radar equivalents in the C-band in comparison to the corresponding (i.e., same underlying size and orientation distributions) legacy T-matrix data. Reflectivity ($Z_\mathrm{H}$) equivalents from T-matrix and DDA scattering calculations are very similar, as expected. Differential reflectivity ($Z_\mathrm{DR}$) and specific differential phase ($K_\mathrm{DP}$), however, exhibit very different characteristics regarding their dependency on mean particle size in the bulk. While the T-matrix data shows high $Z_\mathrm{DR}$ and $K_\mathrm{DP}$ for small mean sizes, maximum values for DDA data occur at large mean sizes. These discrepancies are critical in many applications, like model evaluation or data assimilation, as they will lead to different results and conclusions, e.g., regarding the question of whether the model predicts too large or too small particles. This underlines again the importance of applying more realistic hydrometeor shapes in scattering calculations.
 +
 +<met figure
 +    src="https://www2.meteo.uni-bonn.de/spp2115/lib/exe/fetch.php?media=projects:pristine_fig_5.png"
 +    width="800"
 +    fit="responsive"
 +    title="Figure 5"
 +    caption="Observed (left) as well as T-matrix (middle) and DDA (right) based simulated QVP of $Z_\mathrm{DR}$ (top) and $K_\mathrm{DP}$ (bottom) from the $12^\circ$ elevation scan over DWD C-band radar station Prötzel on 25 July 2017. Blue and yellow dashed boxes indicate the cloud ice and snow dominated layers, respectively."
 +    caption-align="left"
 +    load-animation="zoom-in"
 +    background="white"
 +    zoomable lightbox>
 +
 +The DDA-based cloud ice lookup tables have then been applied in EMVORADO simulations. One case day from the Operation Hydrometeors project with primarily stratiform situations has been used and simulations of the radar observations of the DWD C-band radar network performed based on assimilation-aided ICON forecasts. Time-height displays of quasi-vertical profiles (QVPs) of observed and of simulated T-matrix and DDA-based $Z_\mathrm{DR}$ and $K_\mathrm{DP}$ are shown in Figure 5.
 \\ \\ \\ \\
-<WRAP centeralign> +In the upper, cloud ice dominated layers (above ~6 km), simulations from T-matrix data slightly overestimated $Z_\mathrm{DR}$, and simulations from DDA data show significant overestimations. $K_\mathrm{DP}$ was underestimated with the T-matrix data; the agreement improved with the DDA scattering data, but an underestimation still persists. Replacing the low-density proxies (soft spheroids in T-matrix calculations) with realistically shaped particles, an increase, particularly in $Z_\mathrm{DR}$, was to be expected (e.g., Schrom and Kumjian, 2018). The disagreement with the observations might be both due to the model ICON (e.g., predicting too large crystals as, e.g., also suggested by the microphysical retrievals performed in PROM-Operation Hydrometeors and PROM-PARA, see e.g., (Trömel et al., 2023) and the forward operator EMVORADO (e.g., the too strong orientation of the crystals assumed), and further analysis is ongoing.
-{{ projects:pristine_fig_4.png?direct&800&nolink }} +
-</WRAP> +
-\\ +
-<WRAP tablewidth 60% center>**Figure 4:** Equivalent ZH, ZDR, and KDP from EMVORADO cloud ice bulk scattering lookup tables from operational internal T-matrix calculations and external DDA-based scattering data from the PRISTINE project. Note that at this state, data for small particles (<0.5mm) is lacking, hence results for small Dmean should be taken with care.</WRAP> +
-\\ \\ +
-So far, lookup tables have been produced for cloud ice, i.e. from single crystals scattering properties, illustrated in Figure 4 in the form of radar equivalents in the C-band in comparison to the corresponding (i.e., same underlying size and orientation distributions) legacy T-matrix data. Reflectivity (ZH) equivalents from T-matrix and DDA scattering calculations are very similar, as expected. Differential reflectivity (ZDR) and specific differential phase (KDP), however, exhibit very different characteristics regarding their dependency on mean particle size in the bulk. While the T-matrix data shows high ZDR and KDP for small mean sizes, maximum values for DDA data occur at large mean sizes. These discrepancies are critical in many applications, like model evaluation or data assimilation, as they will lead to different results and conclusions, e.g., regarding the question of whether the model predicts too large or too small particles. This underlines again the importance of applying more realistic hydrometeor shapes in scattering calculations. +
-\\ \\ +
-<WRAP centeralign> +
-{{ projects:pristine_fig_5.png?direct&800&nolink }} +
-</WRAP> +
-\\ +
-<WRAP tablewidth 60% center>**Figure 5:** Observed (left) as well as T-matrix (middle) and DDA (right) based simulated QVP of ZDR (top) and KDP (bottom) from the 12° elevation scan over DWD C-band radar station Prötzel on 25 July 2017. Blue and yellow dashed boxes indicate the cloud ice and snow dominated layers, respectively.</WRAP> +
-\\ \\ +
-The DDA-based cloud ice lookup tables have then been applied in EMVORADO simulations. One case day from the Operation Hydrometeors project with primarily stratiform situations has been used and simulations of the radar observations of the DWD C-band radar network performed based on assimilation-aided ICON forecasts. Time-height displays of quasi-vertical profiles (QVPs) of observed and of simulated T-matrix and DDA-based ZDR and KDP are shown in Figure 5. +
-\\ \\ +
-In the upper, cloud ice dominated layers (above ~6 km), simulations from T-matrix data slightly overestimated ZDR, and simulations from DDA data show significant overestimations. KDP was underestimated with the T-matrix data; the agreement improved with the DDA scattering data, but an underestimation still persists. Replacing the low-density proxies (soft spheroids in T-matrix calculations) with realistically shaped particles, an increase, particularly in ZDR, was to be expected (e.g., Schrom and Kumjian, 2018). The disagreement with the observations might be both due to the model ICON (e.g., predicting too large crystals as, e.g., also suggested by the microphysical retrievals performed in PROM-Operation Hydrometeors and PROM-PARA, see e.g., (Trömel et al., 2023) and the forward operator EMVORADO (e.g., the too strong orientation of the crystals assumed), and further analysis is ongoing.+
 \\ \\ \\ \\
 Because only scattering data for realistically shaped cloud ice, but not for snow, was applied so far, little change is observed in the simulations’ "polarimetric void" layer between 3 – 6 km, where snow dominates. This issue will be tackled in the next step when DDA scattering data for aggregate particles becomes available. Because only scattering data for realistically shaped cloud ice, but not for snow, was applied so far, little change is observed in the simulations’ "polarimetric void" layer between 3 – 6 km, where snow dominates. This issue will be tackled in the next step when DDA scattering data for aggregate particles becomes available.
  • projects/pristine.txt
  • Last modified: 2026/09/12 21:17
  • by ayush