Speaker: Flavio Lehner
Affiliation: NCAR/RAL/CGD
The Southwestern US experiences substantial natural variability in precipitation and temperature, on timescales ranging from daily to decadal. This variability makes the environment for prediction and management of water resources – critical tasks to ensure the well-being of society in this water-scarce region – challenging. Strong trends from the 1980s to the 2010s from cool and wet to warm and dry conditions, have led to intermittent drought conditions and reduced streamflow predictability. These impacts have led to discussion about the role of anthropogenic climate change, and have also led to other initiatives such as policy-driven drought mitigation and new drought adaptation plans issued by federal agencies.
Here I will address three questions that the trends toward drying and warming have prompted: (1) How unusual are these trends? (2) Can we attribute these trends to anthropogenic climate change? (3) How can we use the answers to question (1) and (2) to increase the resilience of society to such trends in the future? Using tree-ring based reconstructions of hydroclimate, I document the influence of precipitation and temperature on streamflow during the several hundred years before instrumental records became available, providing a baseline for the role of internal versus externally forced climate variability. I then use a constructed circulation analog technique and a set of climate model simulations to dissect and attribute the recent trends. Finally, I will illustrate how we can use this information to improve operational seasonal streamflow forecasts in the Southwest, in an attempt to close the notoriously open circle of research-to-operations.
Refreshments: 3:15 PM
Special Day, Time, and Location
Speaker: Brenda Philips
Affiliation: University of Massachusetts, Amherst
When people receive a hazard warning and decide to take protective action, such as sheltering in place, avoiding flooded roads, or protecting property, that decision is not made all at once. Theoretical and empirical research has demonstrated that individuals go through a process that involves receiving the warning, understanding the warning, personalizing the risk, and then taking protective action. Personalizing the risk, the expectation of personal impacts to self, family, property and daily activities, is a critical component of the protective action decision-making process. While hazards research focuses on the cognitive and emotional dimensions of personalization, there is also an important spatial and temporal component. Home, work, and areas of daily activity are physical locations that can be mapped over time to create individual mobility patterns, or footprints. Research in mobility patterns shows that people are creatures of habit and their mobility patterns are largely predictable. If we can predict people’s location and activities at different times of the day, why not use that information for weather alerts and warnings?
This talk will present exploratory research on the potential benefits of incorporating individual footprints into the severe weather warning systems for tornados, flash floods and severe thunderstorms. Our exploratory research examines the potential of warning people based on their individual perceptions and contexts, and how the complexity of human perception and response can be incorporated operationally into warning system technology. Advances in high resolution weather sensing, the Internet of Things (IoT), mobility-enabled Information and Communications Technology (ICT), and high levels of mobile phone usage makes these individualized warnings possible.
This research uses an innovative, multidisciplinary living lab infrastructure located in the Dallas Fort Worth Metroplex in north Texas to explore individualized warning. The CASA Dallas Fort Worth Living Lab for Severe Weather is a sensors-to-human warning system infrastructure where research can be conducted during live severe weather events with stakeholders and the general public. As part of this research platform, we have created a mobile phone app called CASA Alerts that delivers real-time, user-driven weather alerts to the public. The app also functions as a tool for conducting cross-sectional and longitudinal research on human behavior, perception and response.
Refreshments: 1:45 PM
Rescheduled Date from June 14, 2018
Speaker: Michael Ek
Affiliation: NCAR/RAL/JNT
Local land-atmosphere coupling involves the interactions between the land-surface and the atmospheric boundary layer (ABL), and in turn with the free atmosphere above. Initiation of fair-weather cumulus requires an increase in relative humidity at the ABL top, and depends on a number of processes, some opposing each other. Those processes include the evolution of surface fluxes, sub-surface heat and moisture transport, surface-layer turbulence, boundary-layer development, and warm- and dry-air entrainment into the ABL from the free atmosphere above. Following an analytical development, we use modeling and observational data sets to examine this question.
Refreshments: 3:15 PM
Speaker: Prof. Christopher Ruf
Affiliation: University of Michigan
The CYGNSS constellation of eight satellites was successfully launched on 15 December 2016 into a low inclination (tropical) Earth orbit. Each satellite carries a four-channel bistatic radar receiver which measures GPS signals scattered by the ocean, from which ocean surface roughness, near surface wind speed and air-sea latent heat flux, and land surface soil moisture and flood inundation are estimated. The measurements are unique in several respects, most notably in their ability to penetrate through all levels of precipitation, made possible by the low frequency at which GPS operates, and in the frequent sampling of extreme weather events and complete sampling of the diurnal cycle, made possible by the large number of satellites. Engineering commissioning of the constellation was successfully completed in March 2017 and the mission is currently in its science operations phase.
Level 2 science data products have been developed for near surface (10 m referenced) ocean wind speed, ocean surface roughness (mean square slope) and latent heat flux. Level 3 gridded versions of the L2 products have also been developed. A set of Level 4 products have also been developed specifically for direct tropical cyclone overpasses. These include the storm intensity (peak sustained winds) and size (radius of maximum winds), its extent (34, 50 and 64 knot wind radii), and its integrated kinetic energy. Assimilation of CYGNSS L2 wind speed data into the HWRF hurricane weather prediction model has also been developed. Measurements over land demonstrate sensitivity to near-surface soil moisture and the ability to image flood inundation.
An overview and the current status of the mission will be presented, together with highlights of on-orbit performance and recent scientific results.
Refreshments: 3:15 PM
Speaker: Michael Ek
Affiliation: NCAR/RAL/JNT
Local land-atmosphere coupling involves the interactions between the land-surface and the atmospheric boundary layer (ABL), and in turn with the free atmosphere above. Initiation of fair-weather cumulus requires an increase in relative humidity at the ABL top, and depends on a number of processes, some opposing each other. Those processes include the evolution of surface fluxes, sub-surface heat and moisture transport, surface-layer turbulence, boundary-layer development, and warm- and dry-air entrainment into the ABL from the free atmosphere above. Following an analytical development, we use modeling and observational data sets to examine this question.
Refreshments: 3:15 PM
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
Special Day, Time, and Location
Speaker: Brenda Philips
Affiliation: University of Massachusetts, Amherst
When people receive a hazard warning and decide to take protective action, such as sheltering in place, avoiding flooded roads, or protecting property, that decision is not made all at once. Theoretical and empirical research has demonstrated that individuals go through a process that involves receiving the warning, understanding the warning, personalizing the risk, and then taking protective action. Personalizing the risk, the expectation of personal impacts to self, family, property and daily activities, is a critical component of the protective action decision-making process. While hazards research focuses on the cognitive and emotional dimensions of personalization, there is also an important spatial and temporal component. Home, work, and areas of daily activity are physical locations that can be mapped over time to create individual mobility patterns, or footprints. Research in mobility patterns shows that people are creatures of habit and their mobility patterns are largely predictable. If we can predict people’s location and activities at different times of the day, why not use that information for weather alerts and warnings?
This talk will present exploratory research on the potential benefits of incorporating individual footprints into the severe weather warning systems for tornados, flash floods and severe thunderstorms. Our exploratory research examines the potential of warning people based on their individual perceptions and contexts, and how the complexity of human perception and response can be incorporated operationally into warning system technology. Advances in high resolution weather sensing, the Internet of Things (IoT), mobility-enabled Information and Communications Technology (ICT), and high levels of mobile phone usage makes these individualized warnings possible.
This research uses an innovative, multidisciplinary living lab infrastructure located in the Dallas Fort Worth Metroplex in north Texas to explore individualized warning. The CASA Dallas Fort Worth Living Lab for Severe Weather is a sensors-to-human warning system infrastructure where research can be conducted during live severe weather events with stakeholders and the general public. As part of this research platform, we have created a mobile phone app called CASA Alerts that delivers real-time, user-driven weather alerts to the public. The app also functions as a tool for conducting cross-sectional and longitudinal research on human behavior, perception and response.
Refreshments: 1:45 PM
Speaker: Prof. Christopher Ruf
Affiliation: University of Michigan
The CYGNSS constellation of eight satellites was successfully launched on 15 December 2016 into a low inclination (tropical) Earth orbit. Each satellite carries a four-channel bistatic radar receiver which measures GPS signals scattered by the ocean, from which ocean surface roughness, near surface wind speed and air-sea latent heat flux, and land surface soil moisture and flood inundation are estimated. The measurements are unique in several respects, most notably in their ability to penetrate through all levels of precipitation, made possible by the low frequency at which GPS operates, and in the frequent sampling of extreme weather events and complete sampling of the diurnal cycle, made possible by the large number of satellites. Engineering commissioning of the constellation was successfully completed in March 2017 and the mission is currently in its science operations phase.
Level 2 science data products have been developed for near surface (10 m referenced) ocean wind speed, ocean surface roughness (mean square slope) and latent heat flux. Level 3 gridded versions of the L2 products have also been developed. A set of Level 4 products have also been developed specifically for direct tropical cyclone overpasses. These include the storm intensity (peak sustained winds) and size (radius of maximum winds), its extent (34, 50 and 64 knot wind radii), and its integrated kinetic energy. Assimilation of CYGNSS L2 wind speed data into the HWRF hurricane weather prediction model has also been developed. Measurements over land demonstrate sensitivity to near-surface soil moisture and the ability to image flood inundation.
An overview and the current status of the mission will be presented, together with highlights of on-orbit performance and recent scientific results.
Refreshments: 3:15 PM
Speaker: Morris Weisman
Affiliation: NCAR/MMM
Over the past three years, NCAR/MMM has offered access to 48-h forecasts from an experimental 10-member convection-allowing (3-km) ensemble based on the WRF-ARW model, using the Data Assimilation Research Testbed (DART) ensemble Kalman filter approach to produce perturbations for the model initial state. These forecasts have offered new insights into the potential predictability of hazardous convective weather events such as supercells, derechos, and flash flooding, as well as helping to refine the use of ensemble probabilistic guidance for such forecast applications. In this talk, I will review examples of extreme convective events for which forecasts were significantly improved by the use of such a high-resolution ensemble but will also highlight some of the more systematic forecast limitations that were noted over the course of this experiment. One of the more common failure modes was a tendency for the forecast convection to be somewhat north of the observed convection. There was also a tendency for the entire ensemble to be significantly and consistently wrong for the bigger forecast busts. The lack of sufficient ensemble spread for such cases is still under investigation.
Refreshments: 3:15 PM
Speaker: Kevin E. Trenberth
Affiliation: NCAR/ACOM/CGD
Yes and no! Hurricanes are certainly natural, but human-caused climate change is supercharging them, and unbridled growth is exacerbating risk of major damages. The Earth's energy imbalance is caused by increasing greenhouse gases in the atmosphere and its partitioning between atmospheric, ocean, cryosphere and land heat reservoirs govern the rate at which the global climate evolves. Most of the imbalance, over 90%, goes into the ocean and accordingly ocean heat content (OHC) provides a primary indicator of climate change, along with sea level rise. 2017 was the warmest year on record for the global OHC down to 2000 m depth. It fuels storms of all sorts and contributes to very heavy rain events and flooding. The observed increases of upper OHC supports higher sea surface temperatures and atmospheric moisture, and fuels tropical storms to become more intense, bigger and longer lasting, thereby increasing their potential for damage. At the same time sea level is also steadily rising, increasing risks from coastal storm surges. These climatic changes are taking place against a background of growing habitation along coasts, which further increases the risk storms pose to life and property. The damage and loss of life from such storms does not have to be disastrous, however, if there is adequate preparation through better building codes, drainage systems, shelters, and evacuation plans. We have the options of stopping or slowing climate change from humans, and/or adapting to and planning for the consequences, but we are not doing enough of either! Harvey in Houston, Irma in the Caribbean and Florida, and Maria in Puerto Rico are excellent cases in point of the tragedy of global warming.
Refreshments: 3:15 PM
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
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
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
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
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
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
MMM Organizational Chart
MMM Staff & Visitors Directory
MMM Photo Gallery
MMM Laboratory Office
Phone: 303.497.8917
