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We investigate the assimilation of nowcasted information into a classical data assimilation cycle. As a reference setup, we employ the assimilation of standard observations such as direct observations of particular variables into a forecasting system. The pure advective movement extrapolation of observations as a simple nowcasting (NWC) is usually much better for the first minutes to hours, until outperformed by numerical weather prediction (NWP) based on data assimilation. Can nowcasted information be used in the data assimilation cycle? We study both an oscillator model and the Lorenz 63 model with assimilation based on the localized ensemble transform Kalman filter (LETKF). We investigate and provide a mathematical framework for the assimilation of nowcasted information, approximated as a local tendency, into the LETKF in each assimilation step. In particular, we derive and discuss adequate observation error and background uncertainty covariance matrices and interpret the assimilation of nowcasted information as assimilation with an H1-type metric in observation space. Further, we show numerical results that prove that nowcasted information in data assimilation has the potential to significantly improve model based forecasting. [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 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]

Kilometer-scale models allow for an explicit simulation of deep convective overturning but many subgrid processes that are crucial for convective initiation are still poorly represented. This leads to biases such as insufficient convection triggering and late peak of summertime convection. A physically based stochastic perturbation scheme (PSP) for subgrid processes has been proposed (Kober and Craig) that targets the coupling between subgrid turbulence and resolved convection. The first part of this study presents four modifications to this PSP scheme for subgrid turbulence: an autoregressive, continuously evolving random field; a limitation of the perturbations to the boundary layer that removes artificial convection at night; a mask that turns off perturbations in precipitating columns to retain coherent structures; and nondivergent wind perturbations that drastically increase the effectiveness of the vertical velocity perturbations. In a revised version, PSP2, the combined modifications retain the physically based coupling to the boundary layer scheme of the original scheme while removing undesirable side effects. This has the potential to improve predictions of convective initiation in kilometer-scale models while minimizing other biases. The second part of the study focuses on perturbations to account for convective initiation by subgrid orography. Here the mechanical lifting effect is modeled by introducing vertical and horizontal wind perturbations of an orographically induced gravity wave. The resulting perturbations lead to enhanced convective initiation over mountainous terrain. However, the total benefit of this scheme is unclear and we do not adopt the scheme in our revised configuration. [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]

Since radar observations are highly dense in spatial and temporal resolutions, they have been often used to improve short-term numerical weather prediction (NWP) by means of detailed model verification and 3D radar data assimilation. However, the observed quantities are not directly comparable to the prognostic variables of NWP models (e.g. hydrometeor densities, wind vector, temperature, pressure, etc.), so a common approach to facilitate this comparison is to derive synthetic radar observations from model variables; this is the so-called 'radar forward operator'. In the present article, a new Efficient Modular VOlume scanning RADar Operator (EMVORADO) for Doppler velocity and reflectivity is introduced. Although it has been developed in the COSMO model framework, it can be also coupled online to any other NWP model. Comprehensive physical aspects of radar measurements (e.g. beam bending/broadening/shielding, Doppler velocity with fall speed and reflectivity weighting, attenuated reflectivity, detectable signal, etc.) have been implemented in a modular way, using state-of-the-art methods with different levels of approximation and numerical costs that can be optionally chosen. The reflectivity derivation from the prognostic model variables is as 'model consistent' as possible and carefully honours the uncertainties associated with partially melted particles. Efficiency and applicability on supercomputers (MPI-parallelism) is a major design criterion, which allows us to simulate entire networks of 3D volume-scanning meteorological radars within one model run and makes EMVORADO well suited for operational applications. This article aims to give a thorough description of the EMVORADO and to provide a first insight to the performance of different modules by some selected case-studies. [ABSTRACT FROM AUTHOR]

This paper presents the Semi-empirical URban canopY parametrization (SURY) v1.0, which bridges the gap between bulk urban land-surface schemes and explicitcanyon schemes. Based on detailed observational studies, modelling experiments and available parameter inventories, it offers a robust translation of urban canopy parameters - containing the three-dimensional information - into bulk parameters. As a result, it brings canopy-dependent urban physics to existing bulk urban land-surface schemes of atmospheric models. At the same time, SURY preserves a low computational cost of bulk schemes for efficient numerical weather prediction and climate modelling at the convectionpermitting scales. It offers versatility and consistency for employing both urban canopy parameters from bottom-up inventories and bulk parameters from top-down estimates. SURY is tested for Belgium at 2.8 km resolution with the COSMO-CLM model (v5.0_clm6) that is extended with the bulk urban land-surface scheme TERRA_URB (v2.0). The model reproduces very well the urban heat islands observed from in situ urban-climate observations, satellite imagery and tower observations, which is in contrast to the original COSMO-CLM model without an urban land-surface scheme. As an application of SURY, the sensitivity of atmospheric modelling with the COSMO-CLM model is addressed for the urban canopy parameter ranges from the local climate zones of http://WUDAPT.org. City-scale effects are found in modelling the land-surface temperatures, air temperatures and associated urban heat islands. Recommendations are formulated for more precise urban atmospheric modelling at the convection-permitting scales. It is concluded that urban canopy parametrizations including SURY, combined with the deployment of the WUDAPT urban database platform and advancements in atmospheric modelling systems, are essential. [ABSTRACT FROM AUTHOR]

Parametrization of radiation transfer through clouds is an important factor in the ability of Numerical Weather Prediction models to correctly describe the weather evolution. Here we present a practical parameterization of both liquid droplets and ice optical properties in the longwave and shortwave radiation. An advanced spectral averaging method is used to calculate the extinction coefficient, single scattering albedo, forward scattered fraction and asymmetry factor (βext, ϖ, f, g), taking into account the nonlinear effects of light attenuation in the spectral averaging. An ensemble of particle size distributions was used for the ice optical properties calculations, which enables the effective size range to be extended up to 570 μm and thus be applicable for larger hydrometeor categories such as snow, graupel, and rain. The new parameterization was applied both in the COSMO limited-area model and in ICON global model and was evaluated by using the COSMO model to simulate stratiform ice and water clouds. Numerical weather prediction models usually determine the asymmetry factor as a function of effective size. For the first time in an operational numerical weather prediction (NWP) model, the asymmetry factor is parametrized as a function of aspect ratio. The method is generalized and is available on-line to be readily applied to any optical properties dataset and spectral intervals of a wide range of radiation transfer models and applications. [ABSTRACT FROM AUTHOR]