Evaluation of the COSMO model (v5.1) in polarimetric radar space – impact of uncertainties in model microphysics, retrievals and forward operators

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.

Evaluation of convective cloud microphysics in numerical weather predictionmodel with dual-wavelength polarimetric radar observations

<p>The representation of microphysical processes in numerical weather prediction models remains a main source of uncertainty until today. To evaluate the influence of cloud microphysics parameterizations on numerical weather prediction, a convection permitting regional weather model setup has been established using 5 different microphysics schemes of varying complexity (double-moment, spectral bin, particle property prediction (P3)). A polarimetric radar forward operator (CR-SIM) has been applied to simulate radar signals consistent with the simulated particles. The performance of the microphysics schemes is analyzed through a statistical comparison of the simulated radar signals to radar measurements on a dataset of 30 convection days.</p> <p>The observational data basis is provided by two polarimetric research radar systems in the area of Munich, Germany, at C- and Ka-band frequencies and a complementary third polarimetric C-band radar operated by the German Weather Service. By measuring at two different frequencies, the<br />dual-wavelength ratio is derived that facilitates the investigation of the particle size evolution. Polarimetric radars provide in-cloud information about hydrometeor type and asphericity by measuring, e.g., the differential reflectivity ZDR.</p> <p>Within the DFG Priority Programme 2115 PROM, we compare the simulated polarimetric and dual-wavelength radar signals with radar observations of convective clouds. Deviations are found between the schemes and observations in ice and liquid phase, related to the treatment of particle size distributions. Apart from the P3 scheme, simulated reflectivities in the ice phase are too high. Dual-wavelength signatures demonstrate issues of most schemes to correctly represent ice particle size distributions. Comparison of polarimetric radar signatures reveal issues of all schemes except the spectral bin scheme to correctly represent rain particle size distributions. The polarimetric information is further exploited by applying a hydrometeor classification algorithm to obtain dominant hydrometeor classes. By comparing the simulated and observed distribution of hydrometeors, as well as the frequency, intensity and area of high impact weather situations (e.g., hail or heavy convective precipitation), the influence of cloud microphysics on the ability to correctly predict high impact weather situations is examined.</p>

Overview: Fusion of radar polarimetry and numerical atmospheric modelling towards an improved understanding of cloud and precipitation processes

Cloud and precipitation processes are still a main source of uncertainties in numerical weather prediction and climate change projections. The Priority Programme “Polarimetric Radar Observations meet Atmospheric Modelling (PROM)”, funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG), is guided by the hypothesis that many uncertainties relate to the lack of observations suitable to challenge the representation of cloud and precipitation processes in atmospheric models. Such observations can, however, at present be provided by the recently installed dual-polarization C-band weather radar network of the German national meteorological service in synergy with cloud radars and other instruments at German supersites and similar national networks increasingly available worldwide. While polarimetric radars potentially provide valuable in-cloud information on hydrometeor type, quantity, and microphysical cloud and precipitation processes, and atmospheric models employ increasingly complex microphysical modules, considerable knowledge gaps still exist in the interpretation of the observations and in the optimal microphysics model process formulations. PROM is a coordinated interdisciplinary effort to increase the use of polarimetric radar observations in data assimilation, which requires a thorough evaluation and improvement of parameterizations of moist processes in atmospheric models. As an overview article of the inter-journal special issue “Fusion of radar polarimetry and numerical atmospheric modelling towards an improved understanding of cloud and precipitation processes”, this article outlines the knowledge achieved in PROM during the past 2 years and gives perspectives for the next 4 years.

Retrieving microphysical properties of concurrent pristine ice and snow using polarimetric radar observations

Ice and mixed-phase clouds play a key role in our climate system because of their strong controls on global precipitation and radiation budget. Their microphysical properties have been characterized commonly by polarimetric radar measurements. However, there remains a lack of robust estimates of microphysical properties of concurrent pristine ice and aggregates because larger snow aggregates often dominate the radar signal and mask contributions of smaller pristine ice crystals. This paper presents a new method that separates the scattering signals of pristine ice embedded in snow aggregates in scanning polarimetric radar observations and retrieves their respective abundances and sizes for the first time. This method, dubbed ENCORE-ice, is built on an iterative stochastic ensemble retrieval framework. It provides the number concentration, ice water content, and effective mean diameter of pristine ice and snow aggregates with uncertainty estimates. Evaluations against synthetic observations show that the overall retrieval biases in the combined total microphysical properties are within 5 % and that the errors with respect to the truth are well within the retrieval uncertainty. The partitioning between pristine ice and snow aggregates also agrees well with the truth. Additional evaluations against in situ cloud probe measurements from a recent campaign for a stratiform cloud system are promising. Our median retrievals have a bias of 98 % in the total ice number concentration and 44 % in the total ice water content. This performance is generally better than the retrieval from empirical relationships. The ability to separate signals of different ice species and to provide their quantitative microphysical properties will open up many research opportunities, such as secondary ice production studies and model evaluations for ice microphysical processes.

Tornado Formation and Intensity Prediction Using Polarimetric Radar Estimates of Updraft Area

A sample of 198 supercells are investigated to determine if a radar proxy for the area of the storm midlevel updraft may be a skillful predictor of imminent tornado formation and/or peak tornado intensity. A novel algorithm, a modified version of the Thunderstorm Risk Estimation from Nowcasting Development via Size Sorting (TRENDSS) algorithm is used to estimate the area of the enhanced differential radar reflectivity factor (ZDR) column in Weather Surveillance Radar – 1988 Doppler data; the ZDR column area is used as a proxy for the area of the midlevel updraft. The areas of ZDR columns are compared for 154 tornadic supercells and 44 non-tornadic supercells, including 30+ supercells with tornadoes rated EF1, EF2, and EF3; nine supercells with EF4+ tornadoes also are analyzed. It is found that (i) at the time of their peak 0-1 km azimuthal shear, non-tornadic supercells have consistently small (< 20 km2) ZDR column areas while tornadic cases exhibit much greater variability in areas, and (ii) at the time of tornadogenesis, EF3+ tornadic cases have larger ZDR column areas than tornadic cases rated EF1/2. In addition, all nine violent tornadoes sampled have ZDR column areas > 30 km2 at the time of tornadogenesis. However, only weak positive correlation is found between ZDR column area and both radar-estimated peak tornado intensity and maximum tornado path width. Planned future work focused on mechanisms linking updraft size and tornado formation and intensity is summarized and the use of the modified TRENDSS algorithm, which is immune to ZDR bias and thus ideal for real-time operational use, is emphasized.

Evaluation of convective cloud microphysics in numerical weather prediction model with dual-wavelength polarimetric radar observations: methods and examples

The representation of cloud microphysical processes contributes substantially to the uncertainty of numerical weather simulations. In part, this is owed to some fundamental knowledge gaps in the underlying processes due to the difficulty to observe them directly. On the path to close these gaps we present a setup for the systematic characterization of differences between numerical weather model and radar observations for convective weather situations. Radar observations are introduced which provide targeted dual-wavelength and polarimetric measurements of convective clouds with the potential to provide more detailed information about hydrometeor shapes and sizes. A convection permitting regional weather model setup is established using 5 different microphysics schemes (double-moment, spectral bin (FSBM), and particle property prediction (P3)). Observations are compared to hindcasts which are created with a polarimetric radar forward simulator for all measurement days. A cell-tracking algorithm applied to radar and model data facilitates comparison on a cell object basis. Statistical comparisons of radar observations and numerical weather model runs are presented on a dataset of 30 convection days. In general, simulations show too few weak and small-scale convective cells. Contoured frequency by altitude distributions of radar signatures reveal deviations between the schemes and observations in ice and liquid phase. Apart from the P3 scheme, simulated reflectivities in the ice phase are too high. Dual-wavelength signatures demonstrate issues of most schemes to correctly represent ice particle size distributions, producing overly large graupel particles. Comparison of polarimetric radar signatures reveal issues of all schemes except the FSBM to correctly represent rain particle size distributions.