Events (Upcoming & Past)

Past MMM Events

Speaker: Manfred Wendisch
Affiliation: University of Leipzig, Leipzig Institute for Meteorology, Leipzig, Germany

Within the last 25 years a remarkable increase of the Arctic near–surface air temperature exceeding the global warming by a factor of two to three has been observed. This phenomenon is commonly referred to as Arctic Amplification. The Arctic climate has several unique features, for example, the mostly low solar elevation, regularly occurring polar day and night, high surface albedo, large sea ice covered areas, an often shallow atmospheric boundary layer, and the frequent abundance of low–level mixed–phase clouds. These characteristics influence the physical and bio–geochemical processes (such as feedback mechanisms of water vapor, clouds, temperature, and lapse–rate), atmospheric composition (trace gases, aerosol particles, clouds and precipitation), as well as meteorological (including energy fluxes) and surface parameters. In addition, meridional atmospheric and oceanic transports and exchanges between ocean, troposphere, and stratosphere largely control the Arctic climate. Although many individual consequences of changes in the above parameters and processes are known, their combined influence and relative importance for Arctic Amplification are complicated to quantify and difficult to disentangle. As a result, there is no consensus about the mechanisms dominating Arctic Amplification.

To improve this situation the scientific expertise and competency of several German research institutes and three universities are combined in the framework of the Transregional Collaborative Research Centre TR 172. Observations from instrumentation on satellites, aircraft, tethered balloons, research vessels, and a selected set of ground–based sites are being integrated in dedicated campaigns and long–term measurements. The field studies are conducted in different seasons and meteorological conditions, covering a suitably wide range of spatial and temporal scales. They are performed in an international context and in close collaboration with modelling activities.

In particular the presentation will investigate the role of clouds in the Arctic climate system focusing on their radiative effects. Results of the recent, combined field campaigns ACLOUD and PASCAL will be discussed. The measurement strategy, major instrumentation and highlight topics of the preliminary data analysis are presented. These topics include (i) the multi-mode structure of the terrestrial and solar radiative budget below mixed-phase clouds and respective comparisons with high-resolution simulations with the current numerical weather prediction model operationally used by the German Weather Service, (ii) the radiative forcing of low-level clouds from airborne observations, (iii) aerosol, cloud and precipitation measurements, as well as resulting scientific questions.

Refreshments: 3:15

First Name: 
Bobbie
Last Name: 
Weaver
Phone Extension (4 digits): 
8946
Email: 
weaver@ucar.edu
Building:
Room Number: 
1022
Host lab/program/group:
Type of event:
Calendar Timing: 
Thursday, May 10, 2018 - 3:30pm to 4:30pm

Speaker: Annareli Morales University of Michigan  

Atmospheric rivers (ARs) are responsible for 30-50% of the annual precipitation for the U.S. West Coast, mainly through mountain snowfall. When the moist nearly neutral flow associated with these ARs interacts with topography, complex interactions occur between the dynamics, thermodynamics, and cloud microphysics that make it difficult to disentangle the dominant controls on precipitation type, amount, and its location over a mountain. This seminar presents recent work exploring the sensitivity of clouds and precipitation to microphysical parameter perturbations using an idealized modeling framework. Results for the most influential microphysical parameters found in this case (i.e., snow fallspeed coefficient, snow particle density, ice-cloud water collection efficiency, and rain accretion) will be presented. Additionally, experiments are performed to test how an environment with a weaker wind profile and an environment with a lower freezing level impact the microphysical parameter perturbation results. In general, perturbations to microphysical parameters affect the location of peak precipitation, while the total amount of precipitation is more sensitive to environmental parameter perturbations. A preview of current work using the Morris screening method, which is a robust statistical tool allowing for simultaneous perturbation of numerous parameters, will also be shown. Overall these results highlight the complexity of the orographic precipitation response to microphysical parameter changes and suggests that a small subset of the total number of parameters are responsible for most of the microphysics-induced variability in orographic precipitation.

Refreshments: 3:15 PM

Building:
Room Number: 
1022
Type of event:
Will this event be webcast to the public by NCAR|UCAR?: 
Calendar Timing: 
Friday, April 27, 2018 - 3:30am to 4:30am

Speaker: Annareli Morales
University of Michigan  

Atmospheric rivers (ARs) are responsible for 30-50% of the annual precipitation for the U.S. West Coast, mainly through mountain snowfall. When the moist nearly neutral flow associated with these ARs interacts with topography, complex interactions occur between the dynamics, thermodynamics, and cloud microphysics that make it difficult to disentangle the dominant controls on precipitation type, amount, and its location over a mountain. This seminar presents recent work exploring the sensitivity of clouds and precipitation to microphysical parameter perturbations using an idealized modeling framework. Results for the most influential microphysical parameters found in this case (i.e., snow fallspeed coefficient, snow particle density, ice-cloud water collection efficiency, and rain accretion) will be presented. Additionally, experiments are performed to test how an environment with a weaker wind profile and an environment with a lower freezing level impact the microphysical parameter perturbation results. In general, perturbations to microphysical parameters affect the location of peak precipitation, while the total amount of precipitation is more sensitive to environmental parameter perturbations. A preview of current work using the Morris screening method, which is a robust statistical tool allowing for simultaneous perturbation of numerous parameters, will also be shown. Overall these results highlight the complexity of the orographic precipitation response to microphysical parameter changes and suggests that a small subset of the total number of parameters are responsible for most of the microphysics-induced variability in orographic precipitation.

Refreshments: 3:15 PM

First Name: 
Bobbie
Last Name: 
Weaver
Phone Extension (4 digits): 
8946
Email: 
weaver@ucar.edu
Building:
Room Number: 
1022
Host lab/program/group:
Type of event:
Calendar Timing: 
Thursday, April 26, 2018 - 3:30pm to 4:30pm

Speaker: Wei WuUniversity of Wyoming 

The form of cloud particle size distributions (PSDs) is a crucial fundamental assumption for both numerical bulk microphysical parameterization schemes and remote sensing retrievals. In-situ observations collected from various locations and meteorological scenarios show a similar shape of cloud PSDs, based on which various probability distribution functions have been proposed empirically to represent cloud PSDs, including exponential, gamma, lognormal, and Weibull distributions. Theoretical investigations have also been used to determine the form of cloud PSDs by solving the equation governing the change of PSDs. However, the integro-differential equation is too complex to have analytical solutions except for cases with very simple kernels. Therefore, other approaches are needed to explain the observed cloud PSD. Instead of solving the equation analytically, the use of the principle of maximum entropy (MaxEnt) for determining the analytical form of PSDs from a system perspective is examined here. First, the issue of inconsistency under coordinate transformation that arises using the Gibbs/Shannon definition of entropy is identified, and the use of the concept of relative entropy to avoid this problem is discussed. Focusing on cloud physics, the four-parameter generalized gamma distribution is proposed as the analytical form of a PSD using the principle of maximum (relative) entropy with assumptions on power law relations between state variables, scale invariance and a constraint on the expectation of one state variable (e.g. bulk water mass).

To examine the theory, a particle-based model is developed to explore the analytical form of cloud PSDs. The model directly simulates millions of cloud particles under various warm rain microphysical processes, such as diffusional growth, evaporation, stochastic collision-coalescence, spontaneous breakup, and collision-induced breakup. Each model setup is simulated for many realizations to get both mean and fluctuations of cloud properties. To evaluate the performance of the model, numerical simulations are compared against the analytical solutions for a constant kernel and the commonly used Golovin kernel. Furthermore, the simulations using a realistic geometric collection kernel are compared with previous studies using bin microphysical models. The model shows good agreement with the analytical solutions and has better mass conservation compared to previous bin microphysical simulations using a geometric collection kernel. By combing different microphysical processes, the form of the equilibrium PSD found in previous numerical modeling studies of warm rain is then explored with the model by incorporating related microphysical processes.

Refreshments: 3:15 PM

Building:
Room Number: 
1022
Type of event:
Will this event be webcast to the public by NCAR|UCAR?: 
Calendar Timing: 
Friday, April 13, 2018 - 3:30am to 4:30am

Speaker: Wei Wu
University of Wyoming 

The form of cloud particle size distributions (PSDs) is a crucial fundamental assumption for both numerical bulk microphysical parameterization schemes and remote sensing retrievals. In-situ observations collected from various locations and meteorological scenarios show a similar shape of cloud PSDs, based on which various probability distribution functions have been proposed empirically to represent cloud PSDs, including exponential, gamma, lognormal, and Weibull distributions. Theoretical investigations have also been used to determine the form of cloud PSDs by solving the equation governing the change of PSDs. However, the integro-differential equation is too complex to have analytical solutions except for cases with very simple kernels. Therefore, other approaches are needed to explain the observed cloud PSD. Instead of solving the equation analytically, the use of the principle of maximum entropy (MaxEnt) for determining the analytical form of PSDs from a system perspective is examined here. First, the issue of inconsistency under coordinate transformation that arises using the Gibbs/Shannon definition of entropy is identified, and the use of the concept of relative entropy to avoid this problem is discussed. Focusing on cloud physics, the four-parameter generalized gamma distribution is proposed as the analytical form of a PSD using the principle of maximum (relative) entropy with assumptions on power law relations between state variables, scale invariance and a constraint on the expectation of one state variable (e.g. bulk water mass).

To examine the theory, a particle-based model is developed to explore the analytical form of cloud PSDs. The model directly simulates millions of cloud particles under various warm rain microphysical processes, such as diffusional growth, evaporation, stochastic collision-coalescence, spontaneous breakup, and collision-induced breakup. Each model setup is simulated for many realizations to get both mean and fluctuations of cloud properties. To evaluate the performance of the model, numerical simulations are compared against the analytical solutions for a constant kernel and the commonly used Golovin kernel. Furthermore, the simulations using a realistic geometric collection kernel are compared with previous studies using bin microphysical models. The model shows good agreement with the analytical solutions and has better mass conservation compared to previous bin microphysical simulations using a geometric collection kernel. By combing different microphysical processes, the form of the equilibrium PSD found in previous numerical modeling studies of warm rain is then explored with the model by incorporating related microphysical processes.

Refreshments: 3:15 PM

First Name: 
Bobbie
Last Name: 
Weaver
Phone Extension (4 digits): 
8946
Email: 
weaver@ucar.edu
Building:
Room Number: 
1022
Host lab/program/group:
Type of event:
Calendar Timing: 
Thursday, April 12, 2018 - 3:30pm to 4:30pm

Speaker: Marcus van Lier-WalquiNASA/GISS & CCSR, Columbia University 

Weather and climate models have well-known biases in their representation of physical processes. A prime offender is cloud microphysics, owing to the complexity of hydrometeor interactions as well as the approximations that underpin bulk parameterizations. To some extent, models can be improved by finding optimal values for tunable model parameters, and estimating the uncertainty in these parameters — “parametric” uncertainty. Radar observations, including polarimetric radars and radar Doppler spectra, have shown much promise in providing information related to microphysical processes and can thus be leveraged via, e.g., Bayesian estimation, to probabilistically constrain model parameters. A deeper problem is that structural assumptions are typically hard-coded into parameterization schemes, and thus cannot be systematically improved in the same manner, nor can uncertainty associated with these choices be quantified. This fundamental shortcoming of traditional parameterizations motivates the use of multi-physics ensembles in probabilistic weather forecasts — these are, in essence, attempts at spanning both parametric and structural uncertainties in physical parameterizations, but they typically cannot span these uncertainties smoothly or probabilistically. I will present work on a new microphysics scheme, the Bayesian Observationally-constrained Statistical-physical Scheme, or BOSS,  and describe how it was developed specifically to facilitate characterization of parametric and structural uncertainties in a Bayesian framework. An additional benefit of BOSS is that it is “smooth” and therefore amenable to adjoint methods. I will also present work on applications of Bayesian parameter estimation to ice microphysics, cloud property retrievals, and climate model tuning.

Refreshments: 3:15 PM

Building:
Room Number: 
1022
Type of event:
Will this event be webcast to the public by NCAR|UCAR?: 
Calendar Timing: 
Friday, April 6, 2018 - 3:30am to 4:30am

Speaker: Marcus van Lier-Walqui
NASA/GISS & CCSR, Columbia University 

Weather and climate models have well-known biases in their representation of physical processes. A prime offender is cloud microphysics, owing to the complexity of hydrometeor interactions as well as the approximations that underpin bulk parameterizations. To some extent, models can be improved by finding optimal values for tunable model parameters, and estimating the uncertainty in these parameters — “parametric” uncertainty. Radar observations, including polarimetric radars and radar Doppler spectra, have shown much promise in providing information related to microphysical processes and can thus be leveraged via, e.g., Bayesian estimation, to probabilistically constrain model parameters. A deeper problem is that structural assumptions are typically hard-coded into parameterization schemes, and thus cannot be systematically improved in the same manner, nor can uncertainty associated with these choices be quantified. This fundamental shortcoming of traditional parameterizations motivates the use of multi-physics ensembles in probabilistic weather forecasts — these are, in essence, attempts at spanning both parametric and structural uncertainties in physical parameterizations, but they typically cannot span these uncertainties smoothly or probabilistically. I will present work on a new microphysics scheme, the Bayesian Observationally-constrained Statistical-physical Scheme, or BOSS,  and describe how it was developed specifically to facilitate characterization of parametric and structural uncertainties in a Bayesian framework. An additional benefit of BOSS is that it is “smooth” and therefore amenable to adjoint methods. I will also present work on applications of Bayesian parameter estimation to ice microphysics, cloud property retrievals, and climate model tuning.

Refreshments: 3:15 PM

First Name: 
Bobbie
Last Name: 
Weaver
Phone Extension (4 digits): 
8946
Email: 
weaver@ucar.edu
Building:
Room Number: 
1022
Host lab/program/group:
Type of event:
Calendar Timing: 
Thursday, April 5, 2018 - 3:30pm to 4:30pm

Speaker: Andrew HeymsfieldNCAR/MMM  

In this seminar, I will describe the general properties of graupel (rimed particles < 0.5 cm) and hail, based on observations. I will then report on my work that uses novel approaches to estimate the fall characteristics of hail. Three-dimensional volume scans of hailstones of sizes from 2 to 7 cm were printed in 3D models (I’ll show some in my seminar) using ABS plastic, and their terminal velocities were measured in the Mainz vertical wind tunnel. To simulate graupel, some of the hailstone models were printed with dimensions of 0.2-0.5 cm, and their terminal velocities measured. From these experiments, together with earlier observations, I’ve parameterized the properties of graupel and hail for a wide range of particle sizes and heights (pressures) in the atmosphere. The wind tunnel observations, together with the combined total of more than 2800 hailstones for which the mass and cross-sectional area were measured, has been used to develop size-dependent relationships for the terminal velocity, mass flux, and kinetic energy of realistic hailstones.

Also in my seminar, I’ll fill you in on work that I’ve unraveled (going back to data from the mid 1930’s), to try and understand why the insurance and building industries use “outdated” data to estimate and repair hail damage.

Refreshments: 3:15 PM

Building:
Room Number: 
1022
Type of event:
Will this event be webcast to the public by NCAR|UCAR?: 
Calendar Timing: 
Friday, March 30, 2018 - 3:30am to 4:30am

Speaker: Andrew Heymsfield
NCAR/MMM  

In this seminar, I will describe the general properties of graupel (rimed particles < 0.5 cm) and hail, based on observations. I will then report on my work that uses novel approaches to estimate the fall characteristics of hail. Three-dimensional volume scans of hailstones of sizes from 2 to 7 cm were printed in 3D models (I’ll show some in my seminar) using ABS plastic, and their terminal velocities were measured in the Mainz vertical wind tunnel. To simulate graupel, some of the hailstone models were printed with dimensions of 0.2-0.5 cm, and their terminal velocities measured. From these experiments, together with earlier observations, I’ve parameterized the properties of graupel and hail for a wide range of particle sizes and heights (pressures) in the atmosphere. The wind tunnel observations, together with the combined total of more than 2800 hailstones for which the mass and cross-sectional area were measured, has been used to develop size-dependent relationships for the terminal velocity, mass flux, and kinetic energy of realistic hailstones.

Also in my seminar, I’ll fill you in on work that I’ve unraveled (going back to data from the mid 1930’s), to try and understand why the insurance and building industries use “outdated” data to estimate and repair hail damage.

Refreshments: 3:15 PM

First Name: 
Bobbie
Last Name: 
Weaver
Phone Extension (4 digits): 
8946
Email: 
weaver@ucar.edu
Building:
Room Number: 
1022
Host lab/program/group:
Type of event:
Calendar Timing: 
Thursday, March 29, 2018 - 3:30pm to 4:30pm

Charles KnightNCAR/MMM  

“The box” represents classical nucleation theory, CNT, a conceptually simple and at first appealing mechanism in which the interfacial energy between an initial, unstable phase (liquid water, here) and a stable one (ice) constitutes an energy barrier against the stable one’s first appearance.  Ice nucleation obviously involves crystal growth, but the theory of CNT in general has been based upon thermodynamics and chemical reaction theory, independent of crystal structure.  Outside the box here is treating crystal growth and nucleation in terms of growth of the known hydrogen-bond network of ice: the ice crystal structure and its tetrahedral bonding.  The initial context here was trying to explain the observed correlations between ice crystal growth in liquid water and the ice crystal structure, part of which appeared to involve two-dimensional nucleation of new molecular layers at an interface between ice and liquid water.  This explanation turned out not to work well whereas a simple model of growth of the bonding network does seem to provide conceptual understanding.  A bonding-network approach to homogeneous nucleation is unwieldy but interesting, and from that point of view, CNT (for nucleating ice) seems dubious.  The actual mechanism may be dominated by structural effects, not interfacial energy.

Refreshments: 3:15 PM

Building:
Room Number: 
1022
Type of event:
Will this event be webcast to the public by NCAR|UCAR?: 
Calendar Timing: 
Friday, March 23, 2018 - 3:30am to 4:30am

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