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| **Contribution of the Observations Work Package: Spectrally resolved retrieval of hydrometeor shape and orientation**\\ | **Contribution of the Observations Work Package: Spectrally resolved retrieval of hydrometeor shape and orientation**\\ | ||
| - | Recent developments within SPOCC focused on the derivation of multiple hydrometeor species from the range-height-indicator (RHI) scans of the polarimetric hybrid-mode Ka-band cloud radar. This is a delicate task, since the Doppler spectra from the lowest elevation angle of 30° and the highest elevation angle of 90° cannot be compared directly. Horizontal wind effects and differential contributions of the fall velocity of the hydrometeors to the Doppler spectra at the different elevation angles need to be considered. After correction for these effects based on observed profiles of the horizontal wind field, the Doppler spectra at all elevation angels were split into a series of 5 parts. The polarimetric shape and orientation retrieval can then be applied to each of the parts of the Doppler spectrum, separately. An example of the extended shape retrieval is shown for a measurement from Cabauw, NL, taken on 3 November 2014. The 24-hour overview of that day is shown in Fig. 1. A warm-frontal system with embedded precipitation passed the measurement site on that day. The melting layer was located at approximately 1.5 km height, as can be seen from the radar bright band (dark red color). Vertical profiles of the derived polarizability ratio (density-weighted geometric axis ratio) and degree of orientation (deviation of particle orientation from horizontal alignment) are shown in Figure 2. Therein, Figure 2(a) illustrates the result of the plain main-peak retrieval (Myagkov et al., 2016) when applied to a series of 4 RHI scans which were performed between 20:00 and 20:15 UTC. If only the main peak of the Doppler spectrum is considered, a polarizability ratio of approximately 1 (isometric) is derived at almost all heights (except for the invalid region of the melting layer). When the spectrally resolved retrieval is applied to the RHI scan, a different picture is obtained, as Figure 2(b) shows. The slowest falling Doppler spectral part 5 (pink curve) shows particles which are more oblate then the other 4 parts. Also the degree of orientation of part 5 is closer to perfect horizontal alignment (orientation of +1). This illustrates that besides the rather fast falling isometric particles (dominating in parts 1-4 of the Doppler spectrum), also smaller (slower falling) but strongly dendritic (oblate), horizontal aligned particles were present in the observed cloud system. In conclusion, the case study presents the value of the extended shape and orientation retrieval. It can provide valuable insights into the shape partitioning in complex mixed-phase clouds systems, including the detection of regions of secondary ice formation, aggregation or riming. It also enables one to track how ice particle shapes change during precipitation between cloud top to melting layer. The next steps of Work Package 1 comprise the publication of the extended spectrally resolved shape retrieval and the application of the retrieval to prominent case studies of precipitation formation and mixed-phase clouds. | + | Recent developments within SPOCC focused on the derivation of multiple hydrometeor species from the range-height-indicator (RHI) scans of the polarimetric hybrid-mode Ka-band cloud radar. This is a delicate task, since the Doppler spectra from the lowest elevation angle of $30^\circ$ |
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| **Contribution of the Modelling Work Package: Sensitivity of mixed-phase cloud microphysics to aerosol perturbations**\\ | **Contribution of the Modelling Work Package: Sensitivity of mixed-phase cloud microphysics to aerosol perturbations**\\ | ||
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| Next steps will of Work Package 2 will be to couple the output of the simulations to radar forward simulators in order to evaluate the detectability of the aerosol effects on ice microphysics with radar remote sensing techniques. | Next steps will of Work Package 2 will be to couple the output of the simulations to radar forward simulators in order to evaluate the detectability of the aerosol effects on ice microphysics with radar remote sensing techniques. | ||
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| **References: | **References: | ||
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| Based on the ACCEPT dataset, Myagkov et al. (2016a) developed a technique to derive the shape and orientation of cloud hydrometeors. Application of this technique to the peak signal in the observed cloud-radar Doppler spectra yield a statistic about the relationship between shape of ice crystals and temperature in stratiform mixed-phase clouds (Myagkov et al., 2016b). Due to the limitation to the main peak in the Doppler spectrum, the current state of the shape and orientation retrieval does not allow for the retrieval of hydrometor ratios in the observed cloud volume. It is thus goal of the observations part of SPOCC to extend the bulk technique towards the full Doppler spectrum in order to enable the classification of several types of hydrometeors present in the same observation volume. | Based on the ACCEPT dataset, Myagkov et al. (2016a) developed a technique to derive the shape and orientation of cloud hydrometeors. Application of this technique to the peak signal in the observed cloud-radar Doppler spectra yield a statistic about the relationship between shape of ice crystals and temperature in stratiform mixed-phase clouds (Myagkov et al., 2016b). Due to the limitation to the main peak in the Doppler spectrum, the current state of the shape and orientation retrieval does not allow for the retrieval of hydrometor ratios in the observed cloud volume. It is thus goal of the observations part of SPOCC to extend the bulk technique towards the full Doppler spectrum in order to enable the classification of several types of hydrometeors present in the same observation volume. | ||
| - | In a first step, the shape and orientation retrieval of Myagkov et al. (2016a) was reproduced by means of an independent implementation (see Fig. 1), which will be the basis for the future incorporation of the full Doppler spectrum into the shape and orientation retrieval.\\ | + | In a first step, the shape and orientation retrieval of Myagkov et al. (2016a) was reproduced by means of an independent implementation (see Fig. 1), which will be the basis for the future incorporation of the full Doppler spectrum into the shape and orientation retrieval. |
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| Another step of the first project year was to familiarize with the observational datasets of the project which are the prerequisite for the shape and orientation retrieval. Figure 2 shows range-height-indicator (RHI) scans of differential reflectivity and co-cross correlation coefficient as observed at Ka-band during ACCEPT (Fig. 2a) and with the scanning experimental C-Band precipitation radar of the German Meteorological Service (DWD) in Hohenpeißenberg (see Fig. 2b) from which shape and orientation of hydrometeors can be retrieved. The gained experience in application of the shape- and orientation retrieval will in a sub-project be applied to observations of the weather radar network of DWD.\\ | Another step of the first project year was to familiarize with the observational datasets of the project which are the prerequisite for the shape and orientation retrieval. Figure 2 shows range-height-indicator (RHI) scans of differential reflectivity and co-cross correlation coefficient as observed at Ka-band during ACCEPT (Fig. 2a) and with the scanning experimental C-Band precipitation radar of the German Meteorological Service (DWD) in Hohenpeißenberg (see Fig. 2b) from which shape and orientation of hydrometeors can be retrieved. The gained experience in application of the shape- and orientation retrieval will in a sub-project be applied to observations of the weather radar network of DWD.\\ | ||
| - | In the course of the project, the obtained results will be the basis for next step to evaluate parameterizations of cloud microphysical processes in collaboration with the modeling part of SPOCC.\\ | + | In the course of the project, the obtained results will be the basis for next step to evaluate parameterizations of cloud microphysical processes in collaboration with the modeling part of SPOCC. |
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| **Contribution of WP2-Modeling** | **Contribution of WP2-Modeling** | ||
| The first goal of the contribution to SPOCC was an extension of the ice part of the Spectral Microphysics (SPECS) model (Simmel et al., 2017). So far, we have tested the spectral-bin microphysical methodology called AMPS (Advanced Microphysics Prediction System; Hashino et al., 2006) to implement the habit and shape prediction of ice particles in SPECS. Until now, significant efforts have been underway to improve microphysical processes in AMPS, such as the schemes for immersion freezing and habit prediction. Despite these efforts, it is still challenging using modeling alone to resolve such complexity of microphysical processes due the large number of associated parameterizations and assumptions. In particular, the ice habit prediction system in AMPS is sensitive to the 3-D Eulerian advection scheme, such as COSMO. | The first goal of the contribution to SPOCC was an extension of the ice part of the Spectral Microphysics (SPECS) model (Simmel et al., 2017). So far, we have tested the spectral-bin microphysical methodology called AMPS (Advanced Microphysics Prediction System; Hashino et al., 2006) to implement the habit and shape prediction of ice particles in SPECS. Until now, significant efforts have been underway to improve microphysical processes in AMPS, such as the schemes for immersion freezing and habit prediction. Despite these efforts, it is still challenging using modeling alone to resolve such complexity of microphysical processes due the large number of associated parameterizations and assumptions. In particular, the ice habit prediction system in AMPS is sensitive to the 3-D Eulerian advection scheme, such as COSMO. | ||
| - | The steps are as follows. First of all, AMPS was coupled with a simple 1-D dynamic core KiD (Kinematic Driver for microphysics Intercomparison; | + | The steps are as follows. First of all, AMPS was coupled with a simple 1-D dynamic core KiD (Kinematic Driver for microphysics Intercomparison; |
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| **References** | **References** | ||
| * Baldauf, M., Seifert, A., Förstner, J., Majewski, D., Raschendorfer, | * Baldauf, M., Seifert, A., Förstner, J., Majewski, D., Raschendorfer, | ||