Search

A comparison between the spectral microphysics cloud scheme (MiStra) and the parametrized cloud scheme (PaStra) is presented. The main feature of MiStra consists of the treatment of aerosol particles, cloud droplets, and drizzle particles in a joint two-dimensional particle size distribution, whereas PaStra consists of a two-moment scheme for cloud droplets combined with a one-moment scheme for drizzle. Both cloud schemes have been implemented in a single-column model of the cloud-topped marine boundary layer. Numerical sensitivity studies are presented demonstrating that MiStra is capable of simulating in great detail the major cloud microphysical processes occurring in low-level stratiform clouds. While in MiStra no empirical parameter is available to tune the model, the empirical model parameters of PaStra have been tuned by means of the MiStra model results. By comparing the numerical results of PaStra with those of MiStra it is found that PaStra simulates the overall characteristics of the cloud-topped marine boundary layer quite well. At the same time, however, the effects of single cloud microphysical processes differ substantially from those of MiStra. Finally, it is shown that even in PaStra the inclusion of all major cloud microphysical processes is mandatory in order to obtain appropriate results. [ABSTRACT FROM AUTHOR]

The choice and interaction of the parametrization schemes in a numerical weather prediction (NWP) model are crucial for the forecast quality. Because, classically, the parametrization schemes can only communicate to each other via the tendencies of the averaged grid‐scale prognostic variables, a considerable amount of subgrid‐scale information available within the schemes is lost. To overcome this problem, subgrid‐scale information should be directly exchanged between the parametrization schemes. In the present paper already existing subgrid‐scale communication paths, which have been successfully used in operational forecasts in the past, are extended to a physical subgrid‐scale information exchange (PSIE) system including all atmospheric parametrization schemes. For our studies we have chosen the parametrization schemes of microphysics, convection, turbulence, fractional cloud cover and radiation. The COSMO model of Deutscher Wetterdienst (DWD) with a grid size of Δx = 7 km serves as the hosting model for the implementation of the PSIE system. However, the couplings are formulated in a way that also allows the PSIE system to be applied to other NWP models. Simulations of real case‐studies of different weather situations are performed and compared to DWD observational precipitation data from RADOLAN (radar measurements calibrated by station data). Visual inspection of precipitation maps reveals that precipitation patterns look more realistic with the full PSIE system, especially during air‐mass convection situations. This is confirmed by fractional skill score (FSS) analyses, which also give an insight into the temporal development of the PSIE effects. The relative impact of the PSIE paths and their interaction is also studied in conjunction with a factor separation analysis. Usually parametrization schemes in numerical forecast models exchange their subgrid‐scale information via the grid scale. To avoid information loss as a result of Reynolds averaging, our PSIE system provides direct exchange paths as shown in the sketch. As demonstrated with precipitation forecasts of real cases (comparisons by eye, a statistical fraction skill score analysis and a factor separation method), direct communication of subgrid‐scale information by means of a PSIE system can significantly improve the forecast. [ABSTRACT FROM AUTHOR]

Flexible district heating grids form an important part of future, low-carbon energy systems. We examine probabilistic state estimation in such grids, i.e., we aim to estimate the posterior probability distribution over all grid state variables such as pressures, temperatures, and mass flows conditional on measurements of a subset of these states. Since the posterior state distribution does not belong to a standard class of probability distributions, we use Markov Chain Monte Carlo (MCMC) sampling in the space of network heat exchanges and evaluate the samples in the grid state space to estimate the posterior. Converting the heat exchange samples into grid states by solving the non-linear grid equations makes this approach computationally burdensome. However, we propose to speed it up by employing a deep neural network that is trained to approximate the solution of the exact but slow non-linear solver. This novel approach is shown to deliver highly accurate posterior distributions both for classic tree-shaped as well as meshed heating grids, at significantly reduced computational costs that are acceptable for online control. Our state estimation approach thus enables tightening the safety margins for temperature and pressure control and thereby a more efficient grid operation. • Novel approach combining classic statistical analysis and AI. • The estimated state distributions are not standard distributions. • MCMC and Bayesian calculus can be used to approximate state distributions. • MCMC requires mapping demands to grid states in every step. • Deep Neural Networks can speed this up by orders of magnitude. [ABSTRACT FROM AUTHOR]

A new model for nowcasting fog events in the coastal desert area of Dubai is presented, based on a machine-learning algorithm-decision-tree induction. In the investigated region high frequency observations from automatic weather stations were utilized as a database for the analysis of useful patterns. The induced decision trees yield for the first six forecasting hours increased prediction skill when compared to the coupled Weather Research and Forecasting (WRF) model and the PAFOG fog model (Bartok et al., Boundary-Layer Meteorol 145:485-506, ). The decision tree results were further improved by integrating the output of the coupled numerical fog forecasting models in the training database of the decision tree. With this treatment, the statistical quality measures, i.e. the probability of detection, the false alarm ratio, and the Gilbert's skill score, achieved values of 0.88, 0.19, and 0.69, respectively. From these results we conclude that the best fog forecast in the Dubai region is obtained by applying for the first six forecast hours the newly-developed machine-learning algorithm, while for forecast times exceeding 6 h the coupled numerical models are the best choice. [ABSTRACT FROM AUTHOR]

Abstract: For a long time, classical ensemble forecasts have concentrated on simulating the uncertainties of the observations by variations of the initial and boundary conditions. To supplement this with inherent contributions from the forecast model itself, stochastic physics has been proposed as an appropriate approach to improve the forecast quality and to increase the ensemble spread of underdispersive ensembles. Concentrating on cloud processes and their temporal development we here introduce a set of stochastic parameters into existing deterministic physical parameterization schemes as for the trigger function, closure assumption and turbulent mixing in the convection scheme, for the initial ice concentration and turbulent collection kernel in the grid scale precipitation/microphysics scheme and for the subgrid scale cloud cover in the radiation scheme. Considering the stochastic parameters to be subject to unresolved turbulent fluctuations, we derive the corresponding stochastic processes from a Langevin equation based on an approach of direct numeric simulation. This allows for the inclusion of a turbulent autocorrelation time calculated from the eddy dissipation rate and the turbulent kinetic energy of the hosting model. Ensemble simulations of two meteorologicaly different real cases are compared to deterministic control simulations and observations of 1h precipitation sums. The turbulent autocorrelation time shows pronounced spatial variability correlated with frontal dynamics and convective activity. Maps of spatial precipitation patterns and continuous ranked probability score (CRPS) improvements show a better forecast quality of the ensemble mean including stochastic physics. Depending on the meteorological situation, the ensemble spread and the CRPS improvement both exhibit a spin up time and reach a saturation. For air-mass convection/postfrontal showers the spread and the CRPS improvement are higher than for frontal precipitation. The stochastic parameterization of subgrid scale cloud cover is found to degrade the forecast quality of the ensemble. This is explained by a too homogeneous spatial distribution of the added variability. Consequently, this part of the stochastic parameterization has been excluded from the ensemble simulations and is left in its operational deterministic version. [Copyright &y& Elsevier]

• Compares results of spectral microphysical with parametrized fog model. • Shows influence of clouds on evolution of radiation fog. • Uses two-dimensional size distribution for aerosol particles and cloud droplets. • Shows influence of radiation term in droplet growth equation. The spectral microphysics cloud scheme MiFog and the two-moment cloud scheme PaFog are implemented in a single-column model of the atmospheric boundary layer for the numerical simulation of a radiation fog event. Results of numerical sensitivity studies are presented where the influence of cloudiness on the evolution of the fog has been investigated. It is shown that the appearance of cloudiness in the morning hours triggers an efficient dissolution of the fog. During situations with weak turbulence, quasi-periodic oscillations of the liquid water content occur in the MiFog simulations. It turns out that these fluctuations are caused by the direct interaction of diffusional droplet growth and gravitational settling of the droplets. Comparison of the MiFog and the PaFog model runs reveals that most of the time PaFog is capable of simulating the bulk fog properties sufficiently well. [ABSTRACT FROM AUTHOR]

Modern weather prediction models use relatively high grid resolutions as well as sophisticated parametrization schemes for microphysical and other subgrid-scale atmospheric processes. Nonetheless, with these models it remains a difficult task to perform successful numerical fog forecasts since many factors controlling a particular fog event are not yet sufficiently simulated. Here we describe our efforts to create a mechanism that produces successful predictions of fog in the territory located on the north coast of the Arabian Peninsula. Our approach consists in the coupling of the one-dimensional PAFOG fog model with the three-dimensional WRF 3.0 (Weather Research and Forecast) modelling system. The proposed method allows us to construct an efficient operative road traffic warning system for the occurrence of fog in the investigated region. In total 84 historical situations were studied during the period 2008-2009. Moreover, results of operative day-by-day fog forecasting during January and February 2010 are presented. For the investigated arid and hot climate region the land-sea breeze circulation seems to be the major factor affecting the diurnal variations of the meteorological conditions, frequently resulting in the formation of fog. [ABSTRACT FROM AUTHOR]

Abstract: A method is presented to reduce the time-splitting errors of one-dimensional flux form advection schemes. In multidimensional applications deformational correction terms are included in each one-dimensional advection step whereby all correction terms add up to zero. With the correction terms a consistent form of a given one-dimensional advection scheme is achieved when time-splitting is performed. Positive definiteness and shape preservation of the scheme are obtained by nonlinear limitation of the advective fluxes and the deformational correction terms. Large Courant numbers are treated by applying local timestep reduction. The resulting scheme is strictly one-dimensional, mass conservative and positive definite. Shape preservation of the method is nearly achieved. Two- and three-dimensional test runs are presented demonstrating that the method yields satisfactory results. The horizontal flow in a terrain with a steep mountain ridge is investigated by utilizing a terrain following vertical coordinate. It is shown that even in steep orographical terrain the method works quite well, however, small errors occur in the shape preservation of the scheme. These errors increase with increasing spatial variation of the vertical grid mesh. [ABSTRACT FROM AUTHOR]

Abstract: Convection can cause severe precipitation events and is thus of major interest for quantitative precipitation forecasts. Due to its small horizontal scale convection usually has to be parameterized in numerical weather prediction (NWP) models as a subgrid scale phenomenon. Classical mass flux convection schemes assume grid box sizes much larger than the scale of the convective circulation. Thus, the convective mass transport is closed in the local grid column and no net mass transport occurs on the grid scale. In contemporary NWP models with grid sizes of a few kilometers, where convection is already partially resolved, the classical approach leads to a conceptual problem. This can be overcome by a hybrid mass flux convection scheme (HYMACS), in which only the small scale convective updrafts and downdrafts are parameterized, whereas the treatment of the larger scale environmental subsidence is left to the grid scale equations. Different to the classical schemes, HYMACS produces a net convective mass flux exerting pressure gradient forces to the grid scale model. We discuss real cases of different meteorological situations simulated with the operational weather forecast model COSMO of the German Meteorological Service as the hosting model of HYMACS. Precipitation and cloud top pressures simulated with HYMACS are compared with results from classical convection schemes and observational data from the DWD station network and the SEVIRI (MSG) satellite instrument. [Copyright &y& Elsevier]

Abstract: The purpose of this paper is to present a rigorous derivation of a model equation system for the mass and energy open precipitating atmosphere. The derivation is based on the treatment of the atmosphere in terms of three open thermodynamic subsystems consisting of cloudy air, rain water and snow, respectively. Each of the three subsystems moves with a different velocity as caused by the different fall velocities of rain and snow in comparison to the cloudy air. A basic assumption of the model consists in the neglect of the inertia terms in the momentum equation of the falling hydrometeors. It will be shown that this simplification yields additional source terms in the budget equations of the total momentum and the total kinetic energy of the system. However, the prognostic equations for the atmospheric wind field and the corresponding kinetic energy equation remain free of these source terms. The model equations strictly conserve the total mass and energy of the system. It is concluded that the combination of the barycentric velocity of the cloudy air and the total density of the system is the best choice to describe the time evolution of the atmospheric wind field. [Copyright &y& Elsevier]

The new one-dimensional forecast model PAFOG for radiation fogs and low-level stratiform clouds will be presented. The aim of the model is to improve the local visibility forecast on airports and other traffic locations where fog and low-level stratus frequently occur. PAFOG has been developed on the basis of the microphysical fog model MIFOG of Bott et al. [J. Atmos. Sci. 47 (1990) 2153]. To obtain a numerically efficient model, the detailed spectral cloud microphysics of MIFOG has been replaced by the parameterization scheme of Chaumerliac et al. [J. Geophys. Res. 92 (1987) 3114]. Furthermore, according to Siebert et al. [Beitr. Phys. Atmos. 65 (1992a) 93], a model for low vegetation is included in PAFOG so that now fog evolution as influenced by different types of vegetation can also be accounted for.The performance of PAFOG has been tested by comparing the model results with routine observations of the German Weather Service. Nine different weather periods comprising a total of 45 days have been investigated. In 41 cases, PAFOG yields agreement with the observations in terms of occurrence or nonoccurrence of fog or stratiform clouds. During radiation fogs, the calculated and observed visibilities are quite similar. However, in the model simulations the formation of dense fogs tends to be somewhat delayed. From the case studies with stratiform clouds, it is seen that cloud evolution in time and space strongly depends on the value of the large-scale subsidence. Since this quantity is not available from measurements, it must be provided by means of a numerical weather forecast model. [Copyright &y& Elsevier]

Presents the two-component flux method for the numerical solution of the stochastic collection equation in cloud models dealing with two-dimensional cloud microphysics. Description on the two component flux method; Model results; Conclusions.

Presents a mass conservative flux method which can be used as a numerical solution to the stochastic collection equation (SCE). Requirements of numerical modeling of clouds; Information on important mechanisms required for the formation of raindrops; Details on the development of numerical solutions to the SCE problem.

We present MISTRA-v9.0, a one-dimensional (1D) and box (0D) atmospheric chemistry model. The model includes a detailed particle description with regards to the microphysics, gas–particle interactions, and liquid-phase chemistry within particles. Version 9.0 (v9.0) is the first release of MISTRA as an open-source community model. A major review of the code has been performed along with this public version release to improve the user friendliness and platform independence of the model. The purpose of this public release is to maximise the benefit of MISTRA to the community by making the model freely available and easier to use and develop. This paper presents a thorough description of the model characteristics and components. We show some examples of simulations reproducing previous studies with MISTRA, finding that v9.0 is consistent with previous versions. [ABSTRACT FROM AUTHOR]

We present MISTRA-v9.0, a one dimensional (1D) atmospheric chemistry model. The model includes a detailed particle description with regards to the microphysics, gas-particle interactions, and liquid phase chemistry within particles. Version 9.0 is the first release of MISTRA as an open-source community model. A major review of the code has been performed along with this public version release to improve the user-friendliness and platform-independence of the model. In the past 20 years, MISTRA has been used in over 25 studies to address a wide range of scientific questions. The purpose of this public release is to maximise the benefit of MISTRA to the community by making the model freely available and easier to use and develop. This paper presents a thorough description of the model characteristics and components. We show some examples of simulations reproducing previous studies with MISTRA, finding that version 9.0 is consistent with previous versions. [ABSTRACT FROM AUTHOR]

An intensive observation period was conducted in September 2017 in the central Namib, Namibia, as part of the project Namib Fog Life Cycle Analysis (NaFoLiCA). The purpose of the field campaign was to investigate the spatial and temporal patterns of the coastal fog that occurs regularly during nighttime and morning hours. The fog is often linked to advection of a marine stratus that intercepts with the terrain up to 100 km inland. Meteorological data, including cloud base height, fog deposition, liquid water path, and vertical profiles of wind speed/direction and temperature, were measured continuously during the campaign. Additionally, profiles of temperature and relative humidity were sampled during five selected nights with stratus/fog at both coastal and inland sites using tethered balloon soundings, drone profiling, and radiosondes. This paper presents an overview of the scientific goals of the field campaign; describes the experimental setup, the measurements carried out, and the meteorological conditions during the intensive observation period; and presents first results with a focus on a single fog event. [ABSTRACT FROM AUTHOR]

Measurements of the basic meteorological parameters and ultraviolet (UV) erythemalradiation (UVER) were performed in Didcot, England, on 6 and 7 April 2017. The UVERdata was collected by dosimeters, which consist of two sensors (named UVA and UVBC)collecting the erythemally weighted UV-A (320 - 400 nm) and UVBC (UV-B and UV-C, 250- 320 nm) radiation parts, respectively. Both measurement days were characterized by clearconditions during the morning and afternoon hours. Around noon of both days,however, development of shallow stratocumulus clouds (SSC) occurred. In addition, alow-ozone event (ozone mini-hole) took place on 7 April being characterized bya 34 DU (Dobson Unit) drop in total stratospheric ozone content. Compared to6 April, the UVER increase as a result of the low-ozone event amounts to 2.67standard erythema dose (SED) for diffuse and 4.32 SED for global radiation. Thecorresponding radiation amplification factors (RAF) amount to 1.62 and 1.52 fordiffuse and global UVER, respectively. The SSC coverage caused reductions forthe total global UVER doses amounting to 2.33 SED (6 April) and 2.81 SED(7 April). As innovation, we present a decomposition of the RAF into two parts, called cloud ozonefactor (COF) and radiation amplification factor based on measured data (RAFm). The goalof this decomposition is to quantify the low-ozone event’s effect and the SSC influence inindependently modifying the UVER doses. Hereby the newly introduced COFexpresses the weight of each of these two effects acting during the same low-ozoneevent. For the case presented, the COF values range between -0.13 and -0.11 fordiffuse UVER and -0.03 to -0.07 for the global UV and UV-B parts. A positive COFvalue (0.18) results for the global UV-A range. Negative (positive) COF valuescorrespond to a decreased (increased) cloud influence on the second of both referencedays. [ABSTRACT FROM AUTHOR]

Precipitation forms in warm clouds via collision-coalescence. This process is difficult to observe directly in situ and its implementation in numerical models is uncertain. We use aircraft observations of the drop-size distribution (DSD) near marine stratocumulus tops to estimate collision-coalescence rates. Marine stratocumulus is a useful system to study collisional growth because it is initiated near the cloud top and the clouds evolve slowly enough to obtain statistically useful data from aircraft. We compare rate constants estimated from observations with reference rate constants derived from a collision-coalescence box model, the result of which is termed the enhancement factor (EF). We evaluate two hydrodynamic collision-coalescence kernels, one quiescent and one including the effects of small-scale turbulence. Due to sampling volume limitations, DSDs must be averaged over length-scales much greater than those relevant to the underlying physics, such that we also examine the role of averaging length-scale with respect to process representation. Averaging length-scales of 1.5 and 30 km are used, corresponding roughly to the horizontal grid lengths of cloud-resolving models and high-resolution climate models, respectively. EF values range from 0.1 to 40, with the greatest EFs associated with small mode diameter cases and a generally decreasing trend with drop size. For any given drop size or averaging length-scale, there is about an order of magnitude variability in EFs. These results suggest that spatial variability on length-scales smaller than 1.5 km prevents accurate retrieval of rate constants from large-scale average DSDs. Large-scale models must therefore account for small-scale variability to represent cloud microphysical processes accurately. The turbulent kernel reduces EFs for all drop sizes, but can only account for at most half of the calculated EFs in marine stratocumulus. [ABSTRACT FROM AUTHOR]

The numerical prediction of fog requires a very high vertical resolution of the atmosphere. Owing to a prohibitive computational effort of high resolution three dimensional models, operational fog forecast is usually done by means of one dimensional fog models. An important condition for a successful fog forecast with one dimensional models consists of the proper integration of observational data into the numerical simulations. The goal of the present study is to introduce new methods for the consideration of these data in the one dimensional radiation fog model PAFOG. First, it will be shown how PAFOG may be initialized with observed visibilities. Second, a nudging scheme will be presented for the inclusion of measured temperature and humidity profiles in the PAFOG simulations. The new features of PAFOG have been tested by comparing the model results with observations of the German Meteorological Service. A case study will be presented that reveals the importance of including local observations in the model calculations. Numerical results obtained with the modified PAFOG model show a distinct improvement of fog forecasts regarding the times of fog formation, dissipation as well as the vertical extent of the investigated fog events. However, model results also reveal that a further improvement of PAFOG might be possible if several empirical model parameters are optimized. This tuning can only be realized by comprehensive comparisons of model simulations with corresponding fog observations. [ABSTRACT FROM AUTHOR]

Fog and low stratus clouds are a typical feature in coastal deserts. In the hyper-arid Namib Desert at the southwestern African coast, fog is an important source of water for ecosystems. The knowledge of the spatial and temporal patterns of fog in the Namib-region contributes to a deeper understanding of fog processes and fog-related ecosystems and thus is of great ecological and socio-economic interest.The central aim of our study is to understand processes controlling the spatial and temporal development of coastal desert fog in the Namib by means of numerical simulations. Low stratus clouds form at the top of the marine boundary layer over the cold Benguela Current. These low clouds are advected overland by meso-scale circulations. The interaction of turbulent mixing with microphysical and advection processes in the formation, maintenance and dissipation of fog and low clouds in the Namib Desert imposes high requirements on the corresponding parametrizations. Numerical simulations are performed with an extension of the regional weather prediction model COSMO (Consortium for Small-scale Modeling) which is adapted for the application in the Namib region. To account for microphysical processes involved in fog formation, the microphysical parametrization of the one-dimensional fog forecast model PAFOG (PArameterized FOG) has been implemented into COSMO. The resulting fog forecast model COSMO-PAFOG is run with kilometer-scale horizontal resolution.In our study, five case studies are analyzed and the model results are compared to satellite and ground observations obtained during the field campaign within the framework of the NaFoLiCA (Namib Fog Life Cycle Analysis) project. Tuning of turbulence-related model parameters improves the agreement of model simulations with observations. Especially the cloud cover compared to satellite observations and the diurnal cycle of temperature and humidity are better reproduced in the model simulations with adjusted turbulence parameters. The analysis of the thermodynamical processes yielding fog formation reveals that cooling in the atmospheric boundary layer is the main process leading to saturation while moisture changes play a minor part. [ABSTRACT FROM AUTHOR]

The stratocumulus deck over the South Atlantic is regularly advected into the hyper-arid Namib where it appears as fog when it intercepts with the ascending terrain. This fog is a major source of water for plants and animals. The project Namib Fog Life Cycle Analysis (NaFoLiCA) aims at improving the knowledge about the vertical and spatial patterns of this fog through three subprojects using field measurements, remote sensing and numerical modelling.For this purpose, an intensive operation period (IOP) was conducted between 10th September and 5th October 2017 in the central Namib. The measurements were linked to the FogNet network with main activities taking place at the Gobabeb research and training centre. During the IOP, micrometeorological data including cloud base height, fog deposition, liquid water path and vertical profiles of wind speed and direction were measured continuously. Additionally, profiles of temperature and relative humidity were sampled during five selected nights with stratus/fog using tethered balloon soundings, drone profiling and radiosondes. Measurements took place at both coastal and inland sites as well as on different elevation levels in order to gather data on the spatial dynamics of the fog events. Fog droplet distribution was measured with a cloud droplet probe and liquid water content/liquid water flux estimated with a co-located sonic anemometer. The fog events exhibited considerable spatial and temporal variation between single events and within the network. Selected results from the IOP will be presented with the focus on the development of one fog event. [ABSTRACT FROM AUTHOR]