Events (Upcoming & Past)

Upcoming MMM Events

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

2018 NORTH AMERICAN WORKSHOP ON HAIL & HAILSTORMS
AUGUST 14 - 16, 2018, BOULDER, COLORADO
NCAR CENTER GREEN CAMPUS

Across North America, hailstorms are responsible for over $10 billion dollars in annual property damage. The increase in the impact of hailstorms has outpaced advances in detection, forecasting, and mitigation. The National Science Foundation, the National Center for Atmospheric Research and the Insurance Institute for Business & Home Safety are organizing the first North American Workshop on hail, and hailstorms. The workshop will bring together public and private stakeholders to discuss the current state of the science regarding all facets of this peril and provide a look to the future. The workshop will be held at the NCAR Center Green 1 (CG1) campus, 3080 Center Green Drive, Boulder, Colorado.

Call For Abstracts: The deadline for abstract submissions is May 1, 2018.

For information: https://www.mmm.ucar.edu/north-american-hail-workshop 

First Name: 
Kris
Last Name: 
Marwitz
Phone Extension (4 digits): 
8198
Email: 
KMARWITZ@UCAR.EDU
Building:
Room Number: 
Auditorium
Host lab/program/group:
Type of event:
Calendar Timing: 
Repeats every day every Monday and every Tuesday and every Wednesday and every Thursday and every Friday until Thu Aug 16 2018.
Tuesday, August 14, 2018 - 9:00am to Thursday, August 16, 2018 - 5:00pm
Wednesday, August 15, 2018 - 9:00am to Friday, August 17, 2018 - 5:00pm
Thursday, August 16, 2018 - 9:00am to Saturday, August 18, 2018 - 5:00pm

Past MMM Events

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-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 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 Knight
NCAR/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

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 22, 2018 - 3:30pm to 4:30pm

Xiang-Yu Li
Department of Meteorology
Stockholm University, Sweden

We investigate the effect of turbulence on the collisional growth of μm-sized droplets by high-resolution numerical simulations with well resolved Kolmogorov scales, assuming a collision and coalescence efficiency of unity. The droplet dynamics and collisions are approximated using a superparticle approach. We show that the time evolution of the shape of the droplet-size distribution due to turbulence-induced collision depends strongly on the turbulent energy-dissipation rate, but only weakly on the Reynolds number. The size distribution exhibits power law behavior with a slope of −3.7 in the size range of about 10 ∼40 μm, which is close to the power law size distribution found for interstellar dust grains. When gravity is invoked, the strong dependency becomes weakened. Turbulence is found to dominate the time evolution of an initially monodisperse droplet distribution at early times. At later times, however, gravity takes over and dominates the collisional growth. With combined turbulence and gravity, the time scale to reach drizzle sized droplets is about 900 s, which is close to the time scale of rapid warm rain formation. The collision rate grows exponentially, which is consistent with the theoretical prediction of the continuous collisional growth even when the turbulence-generated collision is invoked.

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 8, 2018 - 3:30pm to 4:30pm

Andy Wood
Martyn Clark
NCAR/RAL/HAP 

Ensemble hydrologic (streamflow) prediction provides critical inputs for water, energy and hazard management, particularly in the face of extremes such as floods and droughts.  Following steady advances in operational ensemble numerical weather prediction since the 1990s, US and international operational prediction groups have invested heavily in developing datasets, methods, and models to enable a seamless suite of probabilistic hydrologic predictions spanning timescales from hours to seasons.  Ensemble hydrologic forecasting systems are now operational in a number of countries (including the US), and are enhanced by an increasingly crowded field of operational continental and global ensemble hydrologic prediction services.  In this presentation, we provide background describing the evolution of ensemble hydrologic prediction systems, and highlight the role of the HEPEX (Hydrologic Ensemble Prediction Experiment; www.hepex.org) initiative since 2004 in defining and promoting an integrative, scientific view of the elements of a hydrologic ensemble prediction approach.  These include methods for the probabilistic downscaling and calibration of meteorological forecast ensembles, hydrologic model parameter estimation and uncertainty quantification, hydrologic model data assimilation, model output post-processing, and ensemble forecast verification and communication for use in risk-based decision-making.  We summarize the current state of practice in applying these methods to achieve reliable ensemble streamflow forecasts (locally and globally), and discuss long-standing and new challenges identified by the ensemble hydrologic prediction community.

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 1, 2018 - 3:30pm to 4:30pm

Hugh Morrison
MMM/NCAR

The representation of cloud and precipitation microphysics is a critical element in atmospheric models of all scales. It affects the thermodynamics and dynamics from latent heating/cooling and condensate drag, strongly influences cloudy radiative transfer, and is a key component of the hydrological cycle through the generation and fallout of precipitation. An overview of the historical development of microphysics schemes in cloud and mesoscale models will be presented first. Advances over the last decade will be covered in more detail, particularly the recent development of a scheme called Predicted Particle Properties (P3) that predicts and smoothly evolves ice particle properties such as density and fall speed. This approach is a significant departure from traditional microphysics schemes that separate ice into categories with fixed properties corresponding to particular ice types (small ice, snow, graupel, hail, etc.). Simulations using P3 implemented in the Weather Research and Forecasting (WRF) model will be presented and contrasted with those using traditional schemes. Additional developments related to P3, including an improved numerical treatment of cloud and precipitation transport, will also be presented. Finally, more “outside of the box” ideas for parameterizing microphysics will be highlighted, including a Bayesian statistical-physical parameterization framework that facilitates observational constraint of process rates and a rigorous characterization of uncertainty. The talk with conclude with a broader outlook and commentary on future microphysics scheme developments over the next 5-10 years and beyond.

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, February 15, 2018 - 3:30pm to 4:30pm

Shane Keating
University of New South Wales
Australia  

The era of earth-observing satellites has revolutionised our understanding of our planet and the dynamical processes that shape it. In many real-world geophysical systems, however, estimates of turbulent mixing and transport are limited by the resolution of available observations. In this talk, I will describe a suite of stochastic filtering strategies for estimating mixing in turbulent
geophysical flows from “superresolved” satellite imagery obtained by combining coarse observations with an efficient stochastic parameterization for the unresolved scales.

The method enhances the effective resolution of satellite observations by exploiting the effect of spatial aliasing and generates an optimal estimate of small scales using standard Bayesian inference. The technique is tested in quasigeostrophic simulations driven by realistic climatological shear and stratification profiles. Two applications are considered: calculating poleward ocean eddy heat flux from satellite altimetry, and estimating the three-dimensional upper ocean velocity field from superresolved sea-surface temperature imagery. In each case, the superresolved satellite observations result in a considerable improvement in estimates of turbulent fluxes compared with the raw observations.

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, February 8, 2018 - 3:30pm to 4:30pm

Buo-Fu Chen
National Taiwan University
NCAR/MMM  

Although deep-layer (200−850 hPa) vertical wind shear (VWS) is generally an inhibiting factor for tropical cyclone (TC) intensification, there is still a considerable variability of TC intensification and structural evolution under similar VWS magnitudes. A hypothesis to address this variability is that the interaction between a vertically-sheared TC and the shear-relative low-level mean flow (LMF) modifies the convective structure and its azimuthal distribution, resulting in various pathways of TC structure evolution. This hypothesis was explored from three different perspectives: (1) a global, climatological statistical analysis of the correlations between the 24-hour intensity/size changes and the shear-relative LMF orientations, (2) examining the structural evolution of 180 western North Pacific TCs based on satellite composites, (3) a set of idealized numerical simulations produced with Weather Research and Forecasting (WRF) Model. Based on the best track data of 775 TCs from all basins during 2003−2016, statistical results suggest that a TC affected by an LMF orienting toward down-shear-left favors a relatively large intensification rate, while an LMF orienting toward up-shear-right is favorable for TC expansion. Also, in a storm-motion-relative and shear-relative framework, the analyses based on satellite observations and idealized WRF simulations reveal possible mesoscale processes in the boundary layer causing the distinct convective features associated with TCs affected by various shear-relative LMF. 

Refreshments:  3:15 PM

Note Special Location

First Name: 
Bobbie
Last Name: 
Weaver
Phone Extension (4 digits): 
8946
Email: 
weaver@ucar.edu
Building:
Room Number: 
1001 (Note Location)
Host lab/program/group:
Type of event:
Calendar Timing: 
Thursday, February 1, 2018 - 3:30pm to 4:30pm

Julia Slingo
Chief Scientist, UK Met Office, Emeritus
United Kingdom

Today, we live in a global economy, relying on global trade, efficient transport systems and resilient and reliable provision. As we see time and time again, all these systems are vulnerable to adverse weather and climate conditions. The additional pressure of climate change creates a new set of circumstances and poses new challenges about how secure we will be in the future. More than ever, the weather and climate of food, energy and water have considerable direct and indirect impacts on us – our livelihoods, property, health, well-being and prosperity. In this talk I will describe recent advances in understanding, simulating and predicting our weather and climate and how these developments can be deployed to help us manage our risks, now and in the future.

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, January 25, 2018 - 3:30pm to 4:30pm

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