Application of COSMO-SPECS for remote sensing observations of mixed-phase clouds during CyCare and DACAPO-PESO

2021 ◽  
Author(s):  
Roland Schrödner ◽  
Johannes Bühl ◽  
Fabian Senf ◽  
Oswald Knoth ◽  
Jens Stoll ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Boundary Conditions ◽  
Case Studies ◽  
Weather Prediction ◽  
Cloud Microphysics ◽  
Horizontal Resolution ◽  
Model System ◽  
Mixed Phase ◽  
Real Case ◽  
Mixed Phase Clouds

<p>During the campaigns CyCyare (Limassol, Cyprus) and DACAPO-PESO (Punta Arenas, Chile), remote sensing methods were applied to study mixed-phase clouds. The two sites show contrasting aerosol loads with very clean, marine atmosphere over southern Chile and higher aerosol mass and number concentrations over Cyprus, which frequently are dust-laden. The observations suggest differing cloud properties. To further study the properties and evolution of the observed clouds as well as their relation to the ambient aerosol, the detailed coupled cloud microphysical model COSMO-SPECS is applied for selected real case studies.</p><p>The SPECtral bin cloud microphysicS model SPECS was developed to simulate cloud processes using fixed-bin size distributions of aerosol particles and of liquid and frozen hydrometeors. It was implemented in the numerical weather prediction model COSMO. COSMO-SPECS has been used for idealized case studies with horizontally periodic boundary conditions. Recently, the model system has been enhanced by considering lateral boundary conditions for the hydrometeor spectra allowing for high-resolution real case studies on nested domains. The simulations are carried out by first applying the meteorological driver COSMO using its standard two-moment microphysics scheme on multiple nests with increasing horizontal resolution. Finally, the COSMO-SPECS model system is applied on the innermost domain with a horizontal resolution of a few hundred meters using boundary data derived from the finest driving COSMO domain. For this purpose, the bulk hydrometeor fields of the driving model need to be translated into the corresponding hydrometeor mass and number distributions of SPECS’ hydrometeor spectra.</p><p>In this work, we present first results for selected case-studies of mixed-phase clouds observed during CyCyare and DACAPO-PESO. The results of the model simulations are compared against the LIDAR and cloud radar observations at the two sites.</p>

scholarly journals Hemispheric contrasts in ice formation in stratiform mixed-phase clouds: disentangling the role of aerosol and dynamics with ground-based remote sensing

2021 ◽  
Vol 21 (23) ◽  
pp. 17969-17994
Author(s):  
Martin Radenz ◽  
Johannes Bühl ◽  
Patric Seifert ◽  
Holger Baars ◽  
Ronny Engelmann ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Boundary Layer ◽  
Gravity Wave ◽  
Detection Threshold ◽  
Cloud Microphysics ◽  
Mixed Phase ◽  
Ice Formation ◽  
Absolute Difference ◽  
Optical Parameters ◽  
Mixed Phase Clouds

Abstract. Multi-year ground-based remote-sensing datasets were acquired with the Leipzig Aerosol and Cloud Remote Observations System (LACROS) at three sites. A highly polluted central European site (Leipzig, Germany), a polluted and strongly dust-influenced eastern Mediterranean site (Limassol, Cyprus), and a clean marine site in the southern midlatitudes (Punta Arenas, Chile) are used to contrast ice formation in shallow stratiform liquid clouds. These unique, long-term datasets in key regions of aerosol–cloud interaction provide a deeper insight into cloud microphysics. The influence of temperature, aerosol load, boundary layer coupling, and gravity wave motion on ice formation is investigated. With respect to previous studies of regional contrasts in the properties of mixed-phase clouds, our study contributes the following new aspects: (1) sampling aerosol optical parameters as a function of temperature, the average backscatter coefficient at supercooled conditions is within a factor of 3 at all three sites. (2) Ice formation was found to be more frequent for cloud layers with cloud top temperatures above -15∘C than indicated by prior lidar-only studies at all sites. A virtual lidar detection threshold of ice water content (IWC) needs to be considered in order to bring radar–lidar-based studies in agreement with lidar-only studies. (3) At similar temperatures, cloud layers which are coupled to the aerosol-laden boundary layer show more intense ice formation than decoupled clouds. (4) Liquid layers formed by gravity waves were found to bias the phase occurrence statistics below -15∘C. By applying a novel gravity wave detection approach using vertical velocity observations within the liquid-dominated cloud top, wave clouds can be classified and excluded from the statistics. After considering boundary layer and gravity wave influences, Punta Arenas shows lower fractions of ice-containing clouds by 0.1 to 0.4 absolute difference at temperatures between −24 and -8∘C. These differences are potentially caused by the contrast in the ice-nucleating particle (INP) reservoir between the different sites.

Assessment of the impact of INP and CCN perturbations on mixed-phase cloud microphysics using a spectral-bin model and reference observations

2020 ◽  
Author(s):  
Junghwa Lee ◽  
Patric Seifert ◽  
Tempei Hashino ◽  
Roland Schrödner ◽  
Michael Weger ◽  
...  
Keyword(s):  
Weather Prediction ◽  
Cloud Microphysics ◽  
Mixed Phase ◽  
Doppler Spectrum ◽  
Small Scale ◽  
Prediction System ◽  
Cloud Radar ◽  
Microphysical Processes ◽  
Mixed Phase Clouds ◽  
The Impact

<p>Ice- and mixed-phase clouds largely contribute to global precipitation due to their high spatiotemporal coverage. It has been highlighted that aerosol-cloud interaction is a critical factor. However, our current understanding of the complexity of their microphysical properties is still rather limited.  </p><p>In this talk, we will discuss the impact of perturbations of the cloud condensation nuclei (CCN) and ice-nucleating particle (INP) on the structure and composition of mixed-phase clouds. The main methods are ground-based observations (i.e., Ka-band polarimetric cloud radar) as well as the spectral-bin microphysical methodology called AMPS (Advanced Microphysics Prediction System). 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 endeavors, it is still challenging using modeling alone to resolve such complexity of microphysical processes due to many parameterizations and assumptions. In particular, the ice habit prediction system in AMPS is sensitive to the 3-D Eulerian advection scheme. Meanwhile, the Doppler-spectra derived from polarimetric cloud radar enables us to retrieve the hydrometeor habit of the significant signal peak in the Doppler spectrum of mixed-phase clouds. The synergy between the above mentioned advanced modeling approach and state-of-the-art observation techniques are in our study used to evaluate the effects of the CCN and INP perturbations on mixed-phase clouds. </p><p>The steps are as follows. First of all, we will present the evaluation of a case study of a mixed-phase cloud by observation data. In the course of the work, AMPS is coupled with the German weather prediction system COSMO (Consortium for Small-scale Modeling) model. We choose an observation dataset from the ACCEPT (Analysis of the Composition of Clouds with Extended Polarization Techniques) field campaign in Cabauw, Netherlands, which was conducted during fall 2014. Also, we use the radar forward operator CR-SIM (Cloud Resolving Model Radar Simulator) that translates the dataset of simulation output into radar variables. Therefore, we will present direct comparisons between ground-based observation and modeling datasets. In the next step, AMPS is coupled with a simple 1-D dynamic core KiD (Kinematic Driver for microphysics intercomparison), so-called KiD-AMPS. In doing so, we will discuss the comparison with other schemes (i.e., Morrison 2-moment). Finally, in the frame of KiD-AMPS, we will debate the impact of the CCN and INP perturbations on mixed-phase clouds. </p>

scholarly journals Hemispheric contrasts in ice formation in stratiform mixed-phase clouds: Disentangling the role of aerosol and dynamics with ground-based remote sensing

2021 ◽  
Author(s):  
Martin Radenz ◽  
Johannes Bühl ◽  
Patric Seifert ◽  
Holger Baars ◽  
Ronny Engelmann ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Boundary Layer ◽  
Gravity Wave ◽  
Detection Threshold ◽  
Cloud Microphysics ◽  
Mixed Phase ◽  
Ice Formation ◽  
Absolute Difference ◽  
Optical Parameters ◽  
Mixed Phase Clouds

Abstract. Multi-year ground-based remote-sensing datasets acquired with the Leipzig Aerosol and Cloud Remote Observations System (LACROS) at three sites: a highly polluted central European site (Leipzig, Germany), a polluted and strongly dust-influenced eastern Mediterranean site (Limassol, Cyprus), and a clean marine site in the southern mid-latitudes (Punta Arenas, Chile) are used to contrast ice formation in shallow stratiform liquid clouds. These unique, long-term datasets at key sites of aerosol-cloud interaction provide a deeper insight into cloud microphysics. The influence of temperature, aerosol load, boundary-layer coupling and gravity wave motion on ice formation is investigated. With respect to previous studies of regional contrasts in the properties of mixed-phase clouds our study contributes the following new aspects: (1) Sampling aerosol optical parameters as a function of temperature, the average backscatter coefficient at supercooled temperatures is within a factor of 3 at all three sites. (2) Ice formation was found to be more frequent for cloud layers with cloud top temperatures above −15 °C than indicated by prior lidar-only studies at all sites. A virtual lidar-detection threshold of IWC needs to be considered in order to bring radar-lidar-based studies in agreement with lidar-only studies. (3) At similar temperatures, cloud layers which are coupled to the aerosol-laden boundary layer show more intense ice formation than de-coupled clouds. (4) Liquid layers formed by gravity waves were found to bias the phase occurrence statistics below −15 °C. By applying a novel gravity wave detection approach using vertical velocity observations within the liquid-dominated cloud top, wave clouds can be classified and excluded from the statistics. After considering boundary layer and gravity-wave influences, Punta Arenas shows lower fractions of ice containing clouds by 0.1 to 0.4 absolute difference at temperatures between −24 and −8 °C. These differences are potentially caused by the contrast in the INP reservoir between the different sites.

scholarly journals Predicting the morphology of ice particles in deep convection using the super-droplet method: development and evaluation of SCALE-SDM 0.2.5-2.2.0, -2.2.1, and -2.2.2

2020 ◽  
Vol 13 (9) ◽  
pp. 4107-4157 ◽  
Author(s):  
Shin-ichiro Shima ◽  
Yousuke Sato ◽  
Akihiro Hashimoto ◽  
Ryohei Misumi
Keyword(s):  
Method Development ◽  
Cloud Condensation Nuclei ◽  
Deep Convection ◽  
Cloud Microphysics ◽  
Mixed Phase ◽  
Computational Time ◽  
Ice Particles ◽  
Mass Dimension ◽  
Mixed Phase Clouds ◽  
Droplet Method

Abstract. The super-droplet method (SDM) is a particle-based numerical scheme that enables accurate cloud microphysics simulation with lower computational demand than multi-dimensional bin schemes. Using SDM, a detailed numerical model of mixed-phase clouds is developed in which ice morphologies are explicitly predicted without assuming ice categories or mass–dimension relationships. Ice particles are approximated using porous spheroids. The elementary cloud microphysics processes considered are advection and sedimentation; immersion/condensation and homogeneous freezing; melting; condensation and evaporation including cloud condensation nuclei activation and deactivation; deposition and sublimation; and coalescence, riming, and aggregation. To evaluate the model's performance, a 2-D large-eddy simulation of a cumulonimbus was conducted, and the life cycle of a cumulonimbus typically observed in nature was successfully reproduced. The mass–dimension and velocity–dimension relationships the model predicted show a reasonable agreement with existing formulas. Numerical convergence is achieved at a super-particle number concentration as low as 128 per cell, which consumes 30 times more computational time than a two-moment bulk model. Although the model still has room for improvement, these results strongly support the efficacy of the particle-based modeling methodology to simulate mixed-phase clouds.

scholarly journals Effect of the uncertainty in meteorology on air quality model predictions

Időjárás ◽  
2021 ◽  
Vol 125 (4) ◽  
pp. 625-646
Author(s):  
Zita Ferenczi ◽  
Emese Homolya ◽  
Krisztina Lázár ◽  
Anita Tóth
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Weather Prediction ◽  
Horizontal Resolution ◽  
Model System ◽  
Air Quality Model ◽  
Quality Model ◽  
Near Surface ◽  
The Impact

An operational air quality forecasting model system has been developed and provides daily forecasts of ozone, nitrogen oxides, and particulate matter for the area of Hungary and three big cites of the country (Budapest, Miskolc, and Pécs). The core of the model system is the CHIMERE off-line chemical transport model. The AROME numerical weather prediction model provides the gridded meteorological inputs for the chemical model calculations. The horizontal resolution of the AROME meteorological fields is consistent with the CHIMERE horizontal resolution. The individual forecasted concentrations for the following 2 days are displayed on a public website of the Hungarian Meteorological Service. It is essential to have a quantitative understanding of the uncertainty in model output arising from uncertainties in the input meteorological fields. The main aim of this research is to probe the response of an air quality model to its uncertain meteorological inputs. Ensembles are one method to explore how uncertainty in meteorology affects air pollution concentrations. During the past decades, meteorological ensemble modeling has received extensive research and operational interest because of its ability to better characterize forecast uncertainty. One such ensemble forecast system is the one of the AROME model, which has an 11-member ensemble where each member is perturbed by initial and lateral boundary conditions. In this work we focus on wintertime particulate matter concentrations, since this pollutant is extremely sensitive to near-surface mixing processes. Selecting a number of extreme air pollution situations we will show what the impact of the meteorological uncertainty is on the simulated concentration fields using AROME ensemble members.

scholarly journals The 1-way on-line coupled atmospheric chemistry model system MECO(n) – Part 1: Description of the limited-area atmospheric chemistry model COSMO/MESSy

2012 ◽  
Vol 5 (1) ◽  
pp. 87-110 ◽  
Author(s):  
A. Kerkweg ◽  
P. Jöckel
Keyword(s):  
Boundary Conditions ◽  
Atmospheric Chemistry ◽  
General Circulation ◽  
Weather Prediction ◽  
Model System ◽  
Small Scale ◽  
Limited Area ◽  
Tracer Transport ◽  
Chemistry Research ◽  
Supplementary Document

Abstract. The numerical weather prediction model of the Consortium for Small Scale Modelling (COSMO), maintained by the German weather service (DWD), is connected with the Modular Earth Submodel System (MESSy). This effort is undertaken in preparation of a new, limited-area atmospheric chemistry model. Limited-area models require lateral boundary conditions for all prognostic variables. Therefore the quality of a regional chemistry model is expected to improve, if boundary conditions for the chemical constituents are provided by the driving model in consistence with the meteorological boundary conditions. The new developed model is as consistent as possible, with respect to atmospheric chemistry and related processes, with a previously developed global atmospheric chemistry general circulation model: the ECHAM/MESSy Atmospheric Chemistry (EMAC) model. The combined system constitutes a new research tool, bridging the global to the meso-γ scale for atmospheric chemistry research. MESSy provides the infrastructure and includes, among others, the process and diagnostic submodels for atmospheric chemistry simulations. Furthermore, MESSy is highly flexible allowing model setups with tailor made complexity, depending on the scientific question. Here, the connection of the MESSy infrastructure to the COSMO model is documented and also the code changes required for the generalisation of regular MESSy submodels. Moreover, previously published prototype submodels for simplified tracer studies are generalised to be plugged-in and used in the global and the limited-area model. They are used to evaluate the TRACER interface implementation in the new COSMO/MESSy model system and the tracer transport characteristics, an important prerequisite for future atmospheric chemistry applications. A supplementary document with further details on the technical implementation of the MESSy interface into COSMO with a complete list of modifications to the COSMO code is provided.

scholarly journals Sensitivity of Cloud-Resolving Simulations of Warm-Season Convection to Cloud Microphysics Parameterizations

2007 ◽  
Vol 135 (8) ◽  
pp. 2854-2868 ◽  
Author(s):  
Changhai Liu ◽  
Mitchell W. Moncrieff
Keyword(s):  
Large Scale ◽  
Prediction Models ◽  
Weather Prediction ◽  
Warm Season ◽  
Cloud Microphysics ◽  
Mixed Phase ◽  
Fine Grid ◽  
Upper Level ◽  
Heating Profiles ◽  
Microphysical Parameterization

Abstract This paper investigates the effects of cloud microphysics parameterizations on simulations of warm-season precipitation at convection-permitting grid spacing. The objective is to assess the sensitivity of summertime convection predictions to the bulk microphysics parameterizations (BMPs) at fine-grid spacings applicable to the next generation of operational numerical weather prediction models. Four microphysical parameterization schemes are compared: simple ice (Dudhia), four-class mixed phase (Reisner et al.), Goddard five-class mixed phase (Tao and Simpson), and five-class mixed phase with graupel (Reisner et al.). The experimentation involves a 7-day episode (3–9 July 2003) of U.S. midsummer convection under moderate large-scale forcing. Overall, the precipitation coherency manifested as eastward-moving organized convection in the lee of the Rockies is insensitive to the choice of the microphysics schemes, and the latent heating profiles are also largely comparable among the BMPs. The upper-level condensate and cloudiness, upper-level radiative cooling/heating, and rainfall spectrum are the most sensitive, whereas the domain-mean rainfall rate and areal coverage display moderate sensitivity. Overall, the three mixed-phase schemes outperform the simple ice scheme, but a general conclusion about the degree of sophistication in the microphysics treatment and the performance is not achievable.

Beyond Cloud Microphysics: Representing Subgrid-Scale Processes in Lagrangian Cloud Models

2020 ◽  
Author(s):  
Fabian Hoffmann
Keyword(s):  
Three Dimensional ◽  
Cloud Microphysics ◽  
Mixed Phase ◽  
Droplet Growth ◽  
Subgrid Scale ◽  
Modeling Framework ◽  
Mixing Processes ◽  
New Approach ◽  
Cloud Models ◽  
Mixed Phase Clouds

<p>While the use of Lagrangian cloud microphysical models dates back as far as the 1950s, the integration of this framework into fully-coupled, three-dimensional dynamical models is only possible for about 10 years. In addition to the highly accurate and detailed representation of cloud microphysical processes, these so-called Lagrangian Cloud Models (LCMs) also allow for new ways of representing subgrid-scale dynamical processes and their effects on the microphysical development of clouds, typically neglected or only crudely parameterized due to computational constraints.</p><p>In this talk, I will present a new approach in which supersaturation fluctuations on the subgrid-scale of a large-eddy simulation (LES) model are represented by an economical, one-dimensional model that represents turbulent compression and folding. With a resolution comparable to direct numerical simulation (DNS), inhomogeneous and finite rate mixing processes are explicitly resolved. Applications of this modeling approach for warm-phase shallow cumuli and stratocumuli, and first applications for mixed-phase clouds will be discussed. Generally, clouds susceptible to inhomogeneous mixing show a reduction in the droplet number concentration and stronger droplet growth, in agreement with theory. Stratocumulus entrainment rates tend to be lower using the new approach compared to simulations without it, indicating a more appropriate representation of the entrainment-mixing process. Finally, the Wegner-Bergeron-Findeisen-Process, leading to a rapid ice formation in mixed-phase clouds, is decelerated.</p><p>All in all, this new modeling framework is capable of bridging the gap between LES and DNS, i.e., it enables representing all scales relevant to cloud physics, from entire cloud fields to the smallest turbulent fluctuations, in a single model, allowing to study their interactions explicitly and granting new insights.</p>

scholarly journals Sensitivity of cirrus and mixed-phase clouds to the ice nuclei spectra in McRAS-AC: single column model simulations

2012 ◽  
Vol 12 (6) ◽  
pp. 14927-14957
Author(s):  
R. Morales Betancourt ◽  
D. Lee ◽  
L. Oreopoulos ◽  
Y. C. Sud ◽  
D. Barahona ◽  
...  
Keyword(s):  
Cloud Microphysics ◽  
Ice Nucleation ◽  
Mixed Phase ◽  
Cloud Particle ◽  
Water Path ◽  
Ice Water Path ◽  
Single Column ◽  
Mixed Phase Clouds ◽  
Ice Water ◽  
The Impact

Abstract. The salient features of mixed-phase and ice clouds in a GCM cloud scheme are examined using the ice formation parameterizations of Liu and Penner (LP) and Barahona and Nenes (BN). The performance of LP and BN ice nucleation parameterizations were assessed in the GEOS-5 AGCM using the McRAS-AC cloud microphysics framework in single column mode. Four dimensional assimilated data from the intensive observation period of ARM TWP-ICE campaign was used to drive the fluxes and lateral forcing. Simulation experiments where established to test the impact of each parameterization in the resulting cloud fields. Three commonly used IN spectra were utilized in the BN parameterization to described the availability of IN for heterogeneous ice nucleation. The results show large similarities in the cirrus cloud regime between all the schemes tested, in which ice crystal concentrations were within a factor of 10 regardless of the parameterization used. In mixed-phase clouds there are some persistent differences in cloud particle number concentration and size, as well as in cloud fraction, ice water mixing ratio, and ice water path. Contact freezing in the simulated mixed-phase clouds contributed to transfer liquid to ice efficiently, so that on average, the clouds were fully glaciated at T~260 K, irrespective of the ice nucleation parameterization used. Comparison of simulated ice water path to available satellite derived observations were also performed, finding that all the schemes tested with the BN parameterization predicted average values of IWP within ±15% of the observations.

An evaluation of kilometre scale ICON simulations of mixed-phase stratocumulus over the Southern Ocean during CAPRICORN

2021 ◽  
Author(s):  
Veeramanikandan Ramadoss ◽  
Kevin Pfannkuch ◽  
Alain Protat ◽  
Yi Huang ◽  
Steven Siems ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Boundary Layer ◽  
Southern Ocean ◽  
Climate Models ◽  
Weather Prediction ◽  
Precipitation Amount ◽  
Mixed Phase ◽  
Atmospheric Composition ◽  
Wave Radiation ◽  
Shallow Convection

<p>Stratocumulus (Sc) clouds cover between 25% to 40% of the mid-latitude oceans, where they substantially cool the ocean surface. Many climate models poorly represent these marine boundary layer clouds in the lee of cold fronts in the Southern Ocean (SO), which yields a substantial underestimation of the reflection of short wave radiation. This results in a positive mean bias of 2K in the SO. The representation of stratocumulus clouds, cloud variability, precipitation statistics, and boundary layer dynamics within the ICON-NWP (Icosahedral Nonhydrostatic – Numerical Weather Prediction) model at the km-scale is evaluated in this study over the SO.</p><p>Real case simulations forced by ERA5 are performed with a two-way nesting strategy down to a resolution of 1.2 km. The model is evaluated using the soundings, remote sensing and in-situ observations obtained during the CAPRICORN (Clouds, Aerosols, Precipitation, Radiation, and Atmospheric Composition over the Southern Ocean) field campaign that took place during March and April 2016. During two days (26<sup>th</sup> to 27<sup>th</sup> of March 2016), open-cell stratocumuli were continuously observed by the shipborne radars and lidars between 47<sup>o</sup>S 144<sup>o</sup>E and 45<sup>o</sup>S 146<sup>o</sup>E (South of Tasmania). Our simulations are evaluated against the remote sensing retrievals using the forward simulated radar signatures from PAMTRA (Passive and Active Microwave TRAnsfer).</p><p>The initial results show that the observed variability of various cloud fields is best captured in simulations where only shallow convection is parameterised at this scale. Furthermore, ICON-NWP captures the observed intermittency of precipitation, yet the precipitation amount is overestimated. We further analyse the sensitivity of the cloud and precipitation statistics with respect to primary and secondary ice-phase processes (such as Hallett–Mossop and collisional breakup) in ICON-NWP. Both processes have previously been shown to improve ice properties of simulated shallow mixed-phase clouds over the Southern Ocean in other models. </p>

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