Abstract

POLICE will exploit radar polarimetry for quantitative process studies in mixed phase clouds and for model evaluation. In-situ measurements of microphysical cloud parameters will be performed with the research aircraft HALO and the Falcon in the dendritic ice growth layer (DGL) and the melting layer below to evaluate targeted hypotheses on the origin of enhanced specific differential phase KDP in the DGL and quantify different indicators for the discrimination between aggregation and riming processes in the clouds. This will enable the set-up of a discrimination algorithm based on polarimetric weather radar measurements. Both the explanation of KDP-bands and the ability to distinguish between aggregation and riming is crucial for data assimilation and model microphysics above the melting layer. The in-situ measurements are used to evaluate the state-of the art retrievals of particle number concentration N_t, mean particle diameter D_m and ice water content IWC from the polarimetric radar signals and to evaluate the representation of particle type and size in the ICON-LAM model. The use of advanced spectral bin microphysical schemes in the Hebrew University Cloud Model (HUCM) in combination with measured cloud profiles will also give insights in processes responsible for potential deficiencies in ICON-LAM in order to enhance the representation of the cloud’s microphysical and radar derived properties in ICON-LAM.

Final report 2024

Contribution of University of Bonn

The distinction between aggregation and riming below the dendritic growth layer (DGL) is important, because the latter signals the presence of super-cooled liquid water (SLW). SLW maybe partially responsible for additional ice generation via the Bergeron-Findeisen process. Riming also favors secondary ice production through the Hallet-Mossop ice multiplication process (rime splintering), which is active between -3°C and -8°C. Identification and distinction of regions with dominant riming and aggregation has implications for data assimilation and model microphysics above the melting layer. Following the demand for an area-wide algorithm to distinguish between aggregation and riming exploiting national weather radar networks, POLICE exploited and analyzed quasi-vertical profile (QVP) data of reflectivity (Z_H), differential reflectivity (Z_DR) and in particular depolarization ratio (DR). Similar to Z_DR, DR tends to decrease in rimed snow relative to aggregated snow, but the corresponding difference in DR is 2-4 dB larger (e.g., Ryzhkov et al., 2017). DR can be estimated by dual-polarization radars operating in SHV mode (simultaneous transmission/reception of orthogonally polarized waves) and serve as a proxy for circular depolarization ratio (Matrosov, 2004). Naturally, DR combines the information content of Z_DR and cross-correlation coefficient (ρ_hv) in a single quantity. To set up a riming detection algorithm, the Doppler spectra, available from the vertically pointing C-band radar measurements of the German weather radar network (to date the only operational network providing such data) and post-processed with the procedure described in Gergely et al. (2022) served as input. It is the newly developed mean isolated spectra profile (MISP) technique, which provides clutter free and noise reduced spectra data in a height vs. time format. The MISPs of mean Doppler velocity (MDV) are used to identify regions with particles falling faster than 1.5 m/s and accordingly associated with riming. In addition, the degree of riming of the column above the radar is retrieved by making use of the MDV MISPs by determining the rime mass fraction (RMF) according to Kneifel and Moisseev (2020). Figure 1 shows an investigated test case, which further exhibits a correlation of 0.7 between the Doppler velocities and DR, emphasizing the strong potential of this variable for riming detection.

In order to develop the algorithm, relationships between different polarimetric variables and Doppler velocities can be learned supervised. Four methodologies were tested and trained using QVP data of Z_H, Z_DR, and DR as input from four days containing five precipitation events observed with the DWD Essen radar (ESS) so far. The four methodologies investigated are the gradient boosting model (GBM) based on decision trees, the quadratic discriminant analysis (QDA), the logistic regression (LR) and the multilayer perceptron (MLP) artificial neural network trained with a back propagation algorithm. The ground truth and the results of the riming detection algorithm have been transformed in binary fields: riming (1) and no riming (0). The performances of the resulting algorithms have been evaluated by computing different widely used scores/performance measures such as the confusion matrix, balanced accuracy, Kappa value, F1-score and Matthews correlation coefficient (MCC; Matthews, 1975). The algorithms have been evaluated using 70% of the data for model training and 30% of data for the performance assessment. Prior to training, the data set was prepared to obtain the same ratio of riming to non-riming cases as in the original data set. Due to spatial and temporal mismatches between QVPs and MISPs, due to retrieval uncertainties, and the constraint of pixel-by-pixel comparison arising from the binary analyses, the experimental design does not allow an optimal score of the metrics of 1. However, in summary, the GBM-based riming algorithm performed best on the test data set with a balanced accuracy of 71%, an F1 score of 0.61 and a normalized MCC of 0.73.

In an ensuing step, the retrieval prediction is additionally smoothed in time and height to account for possible mismatches. Overall, the final GBM-based retrieval shows promising results, with a balanced accuracy of 79 %, an F1 score of 0.64 and a normalized MCC of 0.78 when tested with the complete initial input data set. Figure 2 gives an impression of the performance of the new GBM retrieval. In all cases investigated the riming pattern is nicely represented and the retrieval is able to detect riming with a mean RMF of 0.47 (1.5 m/s corresponds to a RMF of approximately 0.46 at C-band).
The extreme stratiform precipitation event observed on 14 July 2021 in western Germany with devastating floods in the Ahr valley in Rhineland-Palatinate was selected to validate the performance of the final GBM retrieval independently. Despite obvious time shifts leading to double penalties affecting the scores, the prediction shows convincing results (see Fig. 3) with a balanced accuracy of 67 %, an F1 score of 0.47 and a normalized MCC of 0.69. These metrics are in the same order of magnitude as those obtained for the test data set.

The validation of the retrieval with more independent cases is still ongoing and the extension of the data set will help to make the algorithm more robust. However, the key development for a radar-based riming detection algorithm has been made.

References:

• Gergely, M., Schaper, M., Toussaint, M., & Frech, M., 2022: Doppler spectra from DWD's operational C-band radar birdbath scan: sampling strategy, spectral postprocessing, and multimodal analysis for the retrieval of precipitation processes. Atmospheric Measurement Techniques, 15(24), 7315-7335, https://doi.org/10.5194/amt-15-7315-2022.
  
• Kneifel, S., & Moisseev, D., 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, https://doi.org/10.1175/JAS-D-20-0007.1.
  
• Matrosov, S. Y., 2004: Depolarization estimates from linear H and V measurements with weather radars operating in simultaneous transmission–simultaneous receiving mode. Journal of Atmospheric and Oceanic Technology, 21(4), 574-583, https://journals.ametsoc.org/view/journals/atot/21/4/1520-0426_2004_021_0574_deflha_2_0_co_2.xml.
  
• Matthews, B. W., 1975: Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochimica et Biophysica Acta (BBA)-Protein Structure, 405(2), 442-451 ,https://doi.org/10.1016/0005-2795(75)90109-9.
  
• Ryzhkov, A., Matrosov, S. Y., Melnikov, V., Zrnic, D., Zhang, P., Cao, Q., … & Troemel, S., 2017: Estimation of depolarization ratio using weather radars with simultaneous transmission/reception. Journal of Applied Meteorology and Climatology, 56(7), 1797-1816, https://doi.org/10.1175/JAMC-D-16-0098.1.
  

Status: January 2023

Contribution of University of Mainz

Within the scope of the POLICE project, the University of Mainz has supported aircraft measurements of clouds in the Arctic and their thermodynamic phase. During the three aircraft field campaigns “Aircraft campaign observing FLUXes of energy and momentum in the cloudy boundary layer over polar sea ice and ocean” (AFLUX), “Atmospheric airborne observations in the Central Arctic” (MOSAiC-ACA; Shupe et al., 2022) and HALO-(AC)3 conducted within the framework of the “Arctic Amplification: Climate Relevant Atmospheric and Surface Processes, and Feedback Mechanisms (AC)3” project (Wendisch et al., 2017), a comprehensive data set of microphysical cloud properties was collected. The research aircraft Polar 5 and 6, operated by the Alfred Wegener Institute (AWI), was used as a platform to conduct in total 33 research flight in the northern Fram Strait between Greenland and Svalbard for remote sensing and in-situ measurements of Arctic clouds.

The in-situ data obtained during AFLUX and MOSAiC-ACA (Mech et al., 2022) have been used to study the microphysical properties and thermodynamic phases of Arctic low-level clouds during the season of maximum and minimum sea ice extend. We investigated the distributions of particle number concentration N, effective diameter Deff and cloud water content CWC (liquid and ice) and developed a method to quantitatively derive the occurrence probability of their thermodynamic phase from the combination of microphysical cloud probe and Polar Nephelometer data. Finally, we assessed changes in cloud microphysics and cloud phase related to the ambient meteorological conditions in spring and summer and addressed effects of the sea ice and open ocean surface conditions. A southerly flow from the sea ice in cold air outbreaks dominates cloud formation processes at temperatures mostly below -10°C in spring, while northerly warm air intrusions favor the formation of liquid clouds at warmer temperatures in summer. We find large differences in the particle sizes in spring and summer and an impact of the surface conditions, which modify the heat and moisture fluxes in the boundary layer. The in-situ data show that mixed-phase clouds are the dominant thermodynamic cloud phase in spring with a frequency of occurrence of 61% over the sea ice and 66% over the ocean. Pure ice clouds exist almost exclusively over the open ocean in spring, and in summer the cloud particles are most likely in the liquid water state (Moser et al., submitted to ACPD, 2023).

Further analyses with cloud data recently collected during HALO-(AC)3 in March and April 2022 will be used to extend the in-situ measured Arctic microphysical cloud statistics. During HALO-(AC)3 three aircraft were flying in formation collecting closely spatially collocated and almost coincident in-situ and remote sensing including non polarimetric radar observations. The data can help to evaluate remote sensing and satellite retrievals (e.g. Klingebiel et al., 2023), and may elucidate the role of clouds in the region of the world with strongest anthropogenic climate change (Wendisch et al., 2022).

References:

• Klingebiel, M., Ehrlich, A., Ruiz-Donoso, E., Risse, N., Schirmacher, I., Jäkel, E., Schäfer, M., Wolf, K., Mech, M., Moser, M., Voigt, C., and Wendisch, M., 2023: Variability and properties of liquid-dominated clouds over the ice-free and sea-ice-covered Arctic Ocean, https://doi.org/10.5194/acp-2022-848.
  
• Mech, M., Ehrlich, A., Herber, A., Lüpkes, C., Wendisch, M., Becker, S., Boose, Y., Chechin, D., Crewell, S., Dupuy, R., Gourbeyre, C., Hartmann, J., Jäkel, E., Jourdan, O., Kliesch, L.-L., Klingebiel, M., Kulla, B. S., Mioche, G., Moser, M., Risse, N., Ruiz-Donoso, E., Schäfer, M., Stapf, J., and Voigt, C., 2022: MOSAiC-ACA and AFLUX - Arctic airborne campaigns characterizing the exit area of MOSAiC, Scientific Data, 9, https://doi.org/10.1038/s41597-022-01900-7.
  
• Shupe, M. D., Rex, M., Blomquist, B., Persson, P. O. G., Schmale, J., Uttal, T., Althausen, D., Angot, H., Archer, S., Bariteau, L., Beck, I., Bilberry, J., Bucci, S., Buck, C., Boyer, M., Brasseur, Z., Brooks, I. M., Calmer, R., Cassano, J., Castro, V., Chu, D., Costa, D., Cox, C. J., Creamean, J., Crewell, S., Dahlke, S., Damm, E., de Boer, G., Deckelmann, H., Dethloff, K., Dütsch, M., Ebell, K., Ehrlich, A., Ellis, J., Engelmann, R., Fong, A. A., Frey, M. M., Gallagher, M. R., Ganzeveld, L., Gradinger, R., Graeser, J., Greenamyer, V., Griesche, H., Griffiths, S., Hamilton, J., Heinemann, G., Helmig, D., Herber, A., Heuzé, C., Hofer, J., Houchens, T., Howard, D., Inoue, J., Jacobi, H.- W., Jaiser, R., Jokinen, T., Jourdan, O., Jozef, G., King, W., Kirchgaessner, A., Klingebiel, M., Krassovski, M., Krumpen, T., Lampert, A., Landing, W., Laurila, T., Lawrence, D., Lonardi, M., Loose, B., Lüpkes, C., Maahn, M., Macke, A., Maslowski, W., Marsay, C., Maturilli, M., Mech, M., Morris, S., Moser, M., Nicolaus, M., Ortega, P., Osborn, J., Pätzold, F., Perovich, D. K., Petäjä, T., Pilz, C., Pirazzini, R., Posman, K., Powers, H., Pratt, K. A., Preußer, A., Quéléver, L., Radenz, M., Rabe, B., Rinke, A., Sachs, T., Schulz, A., Siebert, H., Silva, T., Solomon, A., Sommerfeld, A., Spreen, G., Stephens, M., Stohl, A., Svensson, G., Uin, J., Viegas, J., Voigt, C., von der Gathen, P., Wehner, B., Welker, J. M., Wendisch, M., Werner, M., Xie, Z., and Yue, F., 2022: Overview of the MOSAiC expedition—Atmosphere, Elementa: Science of the Anthropocene, 10, https://doi.org/10.1525/elementa.2021.00060.
  
• Wendisch, M., Brückner, M., Burrows, J., Crewell, S., Dethloff, K., Ebell, K., Lüpkes, C., Macke, A., Notholt, J., Quaas, J., Rinke, A., and Tegen, I., 2017: Understanding Causes and Effects of Rapid Warming in the Arctic, Eos, 98(8), 22-26, https://doi.org/10.1029/2017eo064803.
  
• Wendisch, M., Brückner, M., Crewell, S., Ehrlich, A., Notholt, J., Lüpkes, C., Macke, A., Burrows, J. P., Rinke, A., Quaas, J., Maturilli, M., Schemann, V., Shupe, M. D., Akansu, E. F., Barrientos-Velasco, C., Bärfuss, K., Blechschmidt, A.-M., Block, K., Bougoudis, I., Bozem, H., Böckmann, C., Bracher, A., Bresson, H., Bretschneider, L., Buschmann, M., Chechin, D. G., Chylik, J., Dahlke, S., Deneke, H., Dethloff, K., Donth, T., Dorn, W., Dupuy, R., Ebell, K., Egerer, U., Engelmann, R., Eppers, O., Gerdes, R., Gierens, R., Gorodetskaya, I. V., Gottschalk, M., Griesche, H., Gryanik, V. M., Handorf, D., Harm-Altstädter, B., Hartmann, J., Hartmann, M., Heinold, B., Herber, A., Herrmann, H., Heygster, G., Höschel, I., Hofmann, Z., Hölemann, J., Hünerbein, A., Jafariserajehlou, S., Jäkel, E., Jacobi, C., Janout, M., Jansen, F., Jourdan, O., Jurányi, Z., Kalesse-Los, H., Kanzow, T., Käthner, R., Kliesch, L. L., Klingebiel, M., Knudsen, E. M., Kovács, T., Körtke, W., Krampe, D., Kretzschmar, J., Kreyling, D., Kulla, B., Kunkel, D., Lampert, A., Lauer, M., Lelli, L., von Lerber, A., Linke, O., Löhnert, U., Lonardi, M., Losa, S. N., Losch, M., Maahn, M., Mech, M., Mei, L., Mertes, S., Metzner, E., Mewes, D., Michaelis, J., Mioche, G., Moser, M., Nakoudi, K., Neggers, R., Neuber, R., Nomokonova, T., Oelker, J., Papakonstantinou-Presvelou, I., Pätzold, F., Pefanis, V., Pohl, C., van Pinxteren, M., Radovan, A., Rhein, M., Rex, M., Richter, A., Risse, N., Ritter, C., Rostosky, P., Rozanov, V. V., Donoso, E. R., Saavedra-Garfias, P., Salzmann, M., Schacht, J., Schäfer, M., Schneider, J., Schnierstein, N., Seifert, P., Seo, S., Siebert, H., Soppa, M. A., Spreen, G., Stachlewska, I. S., Stapf, J., Stratmann, F., Tegen, I., Viceto, C., Voigt, C., Vountas, M., Walbröl, A., Walter, M., Wehner, B., Wex, H., Willmes, S., Zanatta, M., and Zeppenfeld, S., 2022: Atmospheric and Surface Processes, and Feedback Mechanisms Determining Arctic Amplification: A Review of First Results and Prospects of the (AC)3 Project, Bulletin of the American Meteorological Society, https://doi.org/10.1175/bams-d-21-0218.1.
  

Status 2021

Joint contribution of University of Bonn and University of Mainz

A large dataset of in-situ microphysical measurements from several flight tracks obtained during the Olympic Mountain Experiment (OLYMPEX; Houze et al., 2017) are analysed to assess the accuracy of state-of-the-art polarimetric microphysical retrievals.

During OLYMPEX, the National Science Foundation (NSF) Doppler On Wheels (DOW; Houze et al. 2018) polarimetric X-band radar performed repeatedly sequences of Range-Height Indicator (RHI) scans within an azimuthal sector of 22 degrees. Similar to Quasi-Vertical Profiles (QVPs; Ryzhkov et al. 2016), Columnar-Vertical Profiles (CVPs; Murphy et al. 2020) and RHI scan-based Vertical Profiles (R-QVP; Allabakash et al. 2019), our technique presents noise-reduced polarimetric variables in a height versus time format. The RHIs performed within an azimuthal sector over a certain time period are averaged within a predefined window size to extract vertical profiles of polarimetric variables at the location of interest. The vertical profiles obtained from ensuing sector-averaged RHIs are combined in a time versus height format and used as data base for polarimetric ice microphysical retrievals. Because this technique considers a more limited spatial domain for averaging, it is well suited to also study flight transects in horizontally less homogeneous cloud fields. During OLYMPEX, the UND Citation research aircraft (Poellot et al. 2017), equipped with advanced measurement devices, flew over the DOW 148 times. The in-situ collected microphysical cloud properties including cloud particle number concentration N_t , particle size distribution, mean volume diameter D_m, and two-dimensional images of hydrometeors are reprocessed and analyzed to enable the direct comparison with the microphysical radar retrievals along 3D trajectories of the research aircraft. For the joint analyses University of Bonn focused on the processing of radar data and the microphysical retrievals, while the University of Mainz processed the in-situ measurements. The combined application of IWC(Zh, Kdp) based on horizontal reflectivity Zh and specific differential phase Kdp (Bukovčić et al. 2018; 2020) in regions where linear differential reflectivity Zdr<0.4 dB and IWC (Zdr, Kdp) (Ryzhkov et al. 2019) elsewhere, as suggested by Carlin et al. (2021), shows the best agreement with the in-situ measurements (Pearson correlation coefficient r= 0.96, RMSE=0.19 g/m³, Fig. 1). The hybrid retrieval by Carlin et al. (2021) outperforms all other aforementioned retrievals and also the non-polarimetric retrieval by Hogan et al. (2006). However, we see a tendency for a slight overestimation of IWC at warmer and an underestimation at colder temperatures.

Polarimetric retrievals for the particle number concentration N_t following Ryzhkov and Zrnić (2019) show more pronounced deviations from in-situ measurements, but still reach a convincing correlation of r=0.88. Overall, the comparison of most recent polarimetric retrievals of mean volume diameter D_m (Ryzhkov et al. 2019), IWC, and N_t with collocated in-situ observations during the OLYMPEX campaign shows agood agreement when certain requirements are fulfilled, e.g. a flight length of at least 30 seconds through the radar sector used for the comparison.

The valuable data set of collocated radar and in-situ measurements extracted from OLYMPEX is further enlarged by aircraft field campaigns including BLUESKY (Voigt et al. 2021) and CIRRUS-HL where in total five different stratiform cloud situations over the DWD polarimetric radar Hohenpeißenberg were sampled with the DLR research airplanes Falcon and HALO. Figure 2 shows a stratiform precipitation event monitored with Hohenpeißenberg radar on 23 May 2020 (right) and collocated (in space and time) with in-situ cloud measurements conducted by the DLR research aircraft Falcon equipped with a Cloud Imaging Probe (left). Measuring microphysical cloud properties at 4 different altitudes, each flight leg is dominated by a different hydrometeor type: mainly aggregates in the highest leg at 4 km above the radar, rimed particles at 3 km, partly melted particles in the melting layer and droplets below.

References:

• Allabakash, S., Lim, S., & Jang, B. J., 2019: Melting layer detection and characterization based on range height indicator–quasi vertical profiles. Remote Sensing, 11(23), 2848.
  
• Bukovčić, P., Ryzhkov, A., & Zrnić, D., 2020: Polarimetric relations for snow estimation—Radar verification. Journal of Applied Meteorology and Climatology, 59(5), 991-1009.
  
• Bukovčić, P., Ryzhkov, A., Zrnić, D., & Zhang, G., 2018: Polarimetric radar relations for quantification of snow based on disdrometer data. Journal of Applied Meteorology and Climatology, 57(1), 103-120.
  
• Carlin, J. T., Reeves, H. D., & Ryzhkov, A. V., 2021: Polarimetric Observations and Simulations of Sublimating Snow: Implications for Nowcasting. Submitted to Journal of Applied Meteorology and Climatology.
  
• Houze Jr, R. A., McMurdie, L. A., Petersen, W. A., Schwaller, M. R., Baccus, W., Lundquist, J. D., … & Chandrasekar, V., 2017: The olympic mountains experiment (OLYMPEX). Bulletin of the American Meteorological Society, 98(10), 2167-2188.
  
• Houze Jr, R. A., Wurman, J., Brodzik, S. and Framback, A., 2018: GPM Ground Validation Doppler on Wheels (DOW) OLYMPEX V2. Dataset available online from the NASA Global Hydrology Resource Center DAAC, Huntsville, Alabama, U.S.A., http://dx.doi.org/10.5067/GPMGV/OLYMPEX/DOW/DATA201.
  
• Murphy, A. M., Ryzhkov, A., & Zhang, P., 2020: Columnar vertical profile (CVP) methodology for validating polarimetric radar retrievals in ice using in situ aircraft measurements. Journal of Atmospheric and Oceanic Technology, 37(9), 1623-1642.
  
• Poellot, M. R., Heymsfield, A. J. and Bansemer, A., 2017: GPM Ground Validation UND Citation Cloud Microphysics OLYMPEX. Dataset available online from the NASA Global Hydrology Resource Center DAAC, Huntsville, Alabama, U.S.A., http://dx.doi.org/10.5067/GPMGV/OLYMPEX/MULTIPLE/DATA201.
  
• Ryzhkov, A., Zhang, P., Reeves, H., Kumjian, M., Tschallener, T., Trömel, S., & Simmer, C., 2016: Quasi-vertical profiles—A new way to look at polarimetric radar data. Journal of Atmospheric and Oceanic Technology, 33(3), 551-562.
  
• Ryzhkov, A. V., & Zrnic, D. S., 2019: Radar polarimetry for weather observations. Springer.
  
• Voigt, C., Lelieveld, J., Schlager, H., Schneider, J., Sauer, D., Meerkötter, R., Pöhlker, M., Bugliaro, L., Curtius, J., Erbertseder, T., Hahn, V., Jöckel, P., Li, Q., Marsing, A., Mertens, M., Pöhlker, C., Pöschl, U., Pozzer, A., Tomsche, L., and Schumann, U., 2021: Aerosol and Cloud Changes during the Corona Lockdown in 2020 – First highlights from the BLUESKY campaign, EGU General Assembly 2021, online, 19–30 April 2021, EGU21-13134, https://doi.org/10.5194/egusphere-egu21-13134.
  

Status 2020

The work in the first year addresses the evaluation of existing in-situ measurements of clouds for radar intercomparison (WP 1). In-situ cloud measurements performed during an overflight of the polarimetric radar station in Bonn (BoXPol) during the ECLIF2/NDMAX flight campaign in February 2018 have been evaluated. The horizontal reflectivities measured by BoXPol along the flight path of the research aircraft NASA DC-8 and the particle number concentrations in the particle size range from 15 to 960 µm measured with the cloud imaging probe CIP onboard the DC-8 are shown in Figure 1 The radar’s blind spot is marked in grey and the red line indicates the time when the radar scan was conducted. In the area close to the radar station a thick cloud layer with particles up to 700 µm in diameter was observed giving rise to the enhanced radar reflectivity. Further away from the radar station the observed cloud particles were smaller and below the detection limit of the radar.
The High Volume Precipitation Spectrometer HVPS has been ordered which will provide a better resolution of microphysical cloud properties detected near polarimetric radar stations in Germany (WP-2). A flight strategy is developed to augment the quality of the in-situ and radar intercomparisons in clouds.

Contribution of University of Mainz

The contribution from the University of Mainz is focusing on airborne in-situ cloud particle measurements over polarimetric radar stations using so-called Optical Array Probes (OAPs). Hereby, microphysical cloud properties are extracted from two dimensional images recorded with OAPs inside clouds (see Fig. 3). During flight, cloud particles pass through a collimated laser beam and their shadows are projected onto a linear array of 128 photodetectors. The presence of a particle is registered by a change in light intensity on each diode. The registered changes in the photodetectors are stored at a rate consistent with the aircraft’s velocity and the instrument’s size resolution. Particle images are reconstructed from individual “slices”, where a slice is the state of the 128-element linear array at a given moment in time. A slice must be stored at each time interval that the particle advances through the beam a distance equal to the resolution of the probe (see Fig. 4 for the working principle of an OAP and recorded images of hydrometeors). Major work contributing to POLICE will be done with the High Volume Precipitation Spectrometer (HVPS) (see Fig. 3), which takes images of cloud particles in the size range from 150 µm to 1.92 cm. From the recorded 2D hydrometeor images, the cloud droplet number concentration, particle size distribution, median volume diameter can be derived. In addition, particles are categorized by hydrometeor type to distinguish between aggregation and riming of large ice crystals. Therefore, this measurement technique is promising to explain the origin of the enhanced specific differential phase KDP and to quantify indicators for the discrimination between aggregation and riming processes in polarimetric radar signals.

References

• Stratton Park Engineering Company. SPEC inc. [Online] .