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Sensitivity experiments with a numerical weather prediction (NWP) model and polarimetric radar forward operator (FO) are conducted for a long-duration stratiform event over northwestern Germany to evaluate uncertainties in the partitioning of the ice water content and assumptions of hydrometeor scattering properties in the NWP model and FO, respectively. Polarimetric observations from X-band radar and retrievals of hydrometeor classifications are used for comparison with the multiple experiments in radar and model space. Modifying the critical diameter of particles for ice-to-snow conversion by aggregation (Dice) and the threshold temperature responsible for graupel production by riming (Tgr), was found to improve the synthetic polarimetric moments and simulated hydrometeor population, while keeping the difference in surface precipitation statistically insignificant at model resolvable grid scales. However, the model still exhibited a low bias (lower magnitude than observation) in simulated polarimetric moments at lower levels above the melting layer (- 3 to - 13 ∘ C) where snow was found to dominate. This necessitates further research into the missing microphysical processes in these lower levels (e.g. fragmentation due to ice–ice collisions) and use of more reliable snow-scattering models to draw valid conclusions. [ABSTRACT FROM AUTHOR]

Sensitivity experiments with a numerical weather prediction (NWP) model and polarimetric radar forward operator (FO) are conducted for a long-duration stratiform event over northwestern Germany, to evaluate uncertainties in the partitioning of the ice water content and assumptions of hydrometeor scattering properties in the NWP model and FO, respectively. Polarimetric observations from X-band radar and retrievals of hydrometeor classifications are used for comparison with the multiple experiments in radar and model space. Modifying two parameters (Dice and Tgr) responsible for the production of snow and graupel, respectively, was found to improve the synthetic polarimetric moments and simulated hydrometeor population, while keeping the difference in surface precipitation statistically insignificant at model resolvable grid scales. However, the model still exhibited a low bias in simulated polarimetric moments at lower levels above the melting layer (-3 to -13 °C) where snow was found to dominate. This necessitates further research into the missing microphysical processes in these lower levels (e.g., fragmentation due to ice-ice collisions), and use of more reliable snow scattering models to draw valid conclusions. [ABSTRACT FROM AUTHOR]

Shallow convection is a subgrid process in cloud-resolving models for which their grid box is larger than the size of small cumulus clouds (Cu). At the same time such Cu substantially affect radiation properties and thermodynamic parameters of the low atmosphere. The main microphysical parameters used for calculation of radiative properties of Cu in cloud-resolving models are liquid water content (LWC), effective droplet radius, and cloud fraction (CF). In this study, these parameters of fields of small, warm Cu are calculated using large-eddy simulations (LESs) performed using the System for Atmospheric Modeling (SAM) with spectral bin microphysics. Despite the complexity of microphysical processes, several fundamental properties of Cu were found. First, despite the high variability of LWC and droplet concentration within clouds and between different clouds, the volume mean and effective radii per specific level vary only slightly. Second, the values of effective radius are close to those forming during adiabatic ascent of air parcels from cloud base. These findings allow for characterization of a cloud field by specific vertical profiles of effective radius and of mean liquid water content, which can be calculated using the theoretical profile of adiabatic liquid water content and the droplet concentration at cloud base. Using the results of these LESs, a simple parameterization of cloud-field-averaged vertical profiles of effective radius and of liquid water content is proposed for different aerosol and thermodynamic conditions. These profiles can be used for calculation of radiation properties of Cu fields in large-scale models. The role of adiabatic processes in the formation of microstructure of Cu is discussed. [ABSTRACT FROM AUTHOR]

For ensemble data assimilation, background error covariance should account for sampling and model errors. There are a number of approaches that have been developed that try to consider these errors; among them, additive noise and relaxation methods (relaxation to prior perturbation and relaxation to prior spread) are often used. In this work, we compare additive noise, based on random samples from global climatological atmospheric background error covariance, to relaxation methods as well as combinations. Our experiments have been conducted in framework of convective‐scale data assimilation with conventional and radar reflectivity observations hourly assimilated for a 2‐week convective period over Germany. In the first week under weather conditions characterized by strong large‐scale forcing of convection, additive noise performs equally or even better than relaxation methods and combinations during both assimilation and short‐range forecasts. In addition, it is shown that the relaxation to prior perturbation may be associated with smoothing of background errors that negatively affect small‐scale structures and that the relaxation to prior spread yields more unbalanced model states. For the second week in absence of strong forcing, the performance of additive noise relative to combinations has been degraded a bit but results are still comparable. Overall, additive noise provides a good benchmark for further developments in representation of model error for convective‐scale data assimilation. Key Points: Additive noise based on climatological background error covariance partially accounts for model error in convective‐scale data assimilationUnder strong forcing conditions, additive noise performs better than relaxation methodsUnder weak forcing conditions, performance of additive noise relative to combinations degrades a bit but results are still comparable [ABSTRACT FROM AUTHOR]

Large-eddy simulations (LES) with the new ICOsahedral Non-hydrostatic atmosphere model (ICON) covering Germany are evaluated for four days in spring 2013 using observational data from various sources. Reference simulations with the established Consortium for Small-scale Modelling (COSMO) numerical weather prediction model and further standard LES codes are performed and used as a reference. This comprehensive evaluation approach covers multiple parameters and scales, focusing on boundary-layer variables, clouds and precipitation. The evaluation points to the need to work on parametrizations influencing the surface energy balance, and possibly on ice cloud microphysics. The central purpose for the development and application of ICON in the LES configuration is the use of simulation results to improve the understanding of moist processes, as well as their parametrization in climate models. The evaluation thus aims at building confidence in the model's ability to simulate small- to mesoscale variability in turbulence, clouds and precipitation. The results are encouraging: the high-resolution model matches the observed variability much better at small- to mesoscales than the coarser resolved reference model. In its highest grid resolution, the simulated turbulence profiles are realistic and column water vapour matches the observed temporal variability at short time-scales. Despite being somewhat too large and too frequent, small cumulus clouds are well represented in comparison with satellite data, as is the shape of the cloud size spectrum. Variability of cloud water matches the satellite observations much better in ICON than in the reference model. In this sense, it is concluded that the model is fit for the purpose of using its output for parametrization development, despite the potential to improve further some important aspects of processes that are also parametrized in the high-resolution model. [ABSTRACT FROM AUTHOR]

Abstract: A hailstorm that caused significant damage in South-West Germany was simulated with the numerical weather prediction model COSMO. To cover hail evolution a sophisticated two-moment cloud microphysical scheme was extended by a particle class representing hail and implemented into COSMO. The horizontal resolution was 1km. For initialization and boundary values COSMO forecasts with a coarser resolution and the standard one-moment microphysical scheme were used. Running this model system several convective cells develop including a severe hailstorm that resembles the observations qualitatively well and produces realistic amounts of precipitation and hail at the ground. Sensitivity studies were conducted varying the concentration of cloud condensation nuclei (CCN) and the shape of the cloud droplet size distribution (CDSD). Results show that both have a significant impact on hail accumulated at the ground and on the size of the hailstones. For two of the three CDSDs assumed the intensity of the severe storm decreases with increasing CCN concentration. However, this is not true for some of the weaker storms that form as well as for the third CDSD. Two model runs are analyzed and compared in more detail revealing the strong coupling between the numerous microphysical processes and between microphysics and dynamics. The sensitivity studies illustrate that the complexity of such storms makes it difficult to foresee, what will happen, when one microphysical parameter is changed. Thus, general conclusions whether an increase or decrease in CCN concentration invigorates a hailstorm cannot be drawn. [Copyright &y& Elsevier]

The performance of the laser-optical Particle Size Velocity (PARSIVEL) disdrometer is evaluated to determine the characteristics of falling snow. PARSIVEL’s measuring principle is reexamined to detect its limitations and pitfalls when applied to solid precipitation. This study uses snow observations taken during the Canadian Cloudsat/Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Validation Project (C3VP) campaign, when two PARSIVEL instruments were collocated with a single two-dimensional disdrometer (2-DVD), which allows more detailed observation of snowflakes. When characterizing the snowflake size, PARSIVEL instruments inherently retrieve only one size parameter, which is approximately equal to the widest horizontal dimension (more accurately with large snowflakes) and that has no microphysical meaning. Unlike for raindrops, the equivolume PARSIVEL diameter—the PARSIVEL output variable—has no physical counterpart for snowflakes. PARSIVEL’s fall velocity measurement may not be accurate for a single snowflake particle. This is due to the internally assumed relationship between horizontal and vertical snow particle dimensions. The uncertainty originates from the shape-related factor, which tends to depart more and more from unity with increasing snowflake sizes and can produce large errors. When averaging over a large number of snowflakes, the correction factor is size dependent with a systematic tendency to an underestimation of the fall speed (but never exceeding 20%). Compared to a collocated 2-DVD for long-lasting events, PARSIVEL seems to overestimate the number of small snowflakes and large particles. The disagreement between PARSIVEL and 2-DVD snow measurements can only be partly ascribed to PARSIVEL intrinsic limitations (border effects and sizing problems), but it has to deal with the difficulties and drawbacks of both instruments in fully characterizing snow properties. [ABSTRACT FROM AUTHOR]

Since 2017, the SINFONY (Seamless INtegrated FOrecastiNg sYstem) project has been under development at the Deutscher Wetterdienst (DWD). It is aimed to provide a seamless ensemble system for early predictions and warnings of severe weather events by combining the nowcasting based on extrapolating observed radar reflectivity and short-term forecasts initiated from the Rapid Update Cycle (RUC) of data assimilation for the convection-permitting ICON (ICOsahedral Nonhydtostatic) model. So far, the ICON-RUC setup has been extensively tested for convective summer cases. In this study, a series of sensitivity experiments have been conducted for the winter precipitation, including the choice of microphysics schemes and the Latent Heat Nudging (LHN). Results show that within data assimilation cycles the two-moment scheme outperforms the one-moment scheme, and the LHN has also positive impacts. For the 6-h reflectivity forecasts, the two-moment scheme is clearly better than the one-moment scheme and the added values by using the LHN persist almost 6 h. For the precipitation forecasts, the two-moment scheme also exhibits advantage for the light precipitation, however, for the moderate precipitation, the one-moment scheme prevails. Current results indicate that the two-moment has to be enhanced for the moderate precipitation in winter. • A series of sensitivity experiments for radar data assimilation have been conducted for winter precipitation. • The two-moment scheme outperforms the one-moment scheme in 6-h forecasts for reflectivity but not for medium precipitation. • The latent heat nudging exhibits an added value in improving the re-flectivity and precipitation forecasts. [ABSTRACT FROM AUTHOR]

As high-resolution climate models become increasingly complex, it should be carefully assessed to what extent the improved physics in such models justifies the large computational cost that the complexity imposes. This paper presents a detailed sensitivity study of convective precipitation characteristics to the number of prognostic moments in bulk microphysics schemes using a high-resolution convection-resolving climate model. It is shown that 1-moment and 2-moment microphysics schemes produce more similar surface precipitation characteristics for a composite of 20 real-case convective simulations in Belgium than for many idealized studies conducted before. In the baseline 2-moment scheme, size sorting of particles is counteracted by collisional drop breakup to produce mean drop sizes that are similar to those in the 1-moment version of the scheme. Hence, fallout, evaporation and surface rain rates are very similar between the two versions of the scheme. Conversely, larger sensitivities of precipitation extremes were found to the treatment of drop breakup and the shape of the particle size distributions. Consistent with previous studies, domain-averaged and peak precipitation increased monotonically with increasing breakup equilibrium diameter Deq. Further, it is shown that a negative exponential size distribution results in excessive radar reflectivities for light rain rates. Surface precipitation and the joint distribution of reflectivity and rain rate are best reproduced by a 2-moment version of the scheme that applies gamma distributions with a diagnostic shape parameter for all particles and a large Deq. However, given the large sensitivities and uncertainties associated with collisional drop breakup and size sorting, it is likely that the full potential of improved physics in a 2-moment scheme will remain underexposed as long as these processes are not better understood. [ABSTRACT FROM AUTHOR]