Upcoming Events + Announcements

Recent (past) Events

  • 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

    First Name: BobbieLast Name: WeaverPhone Extension (4 digits): 8946Email: weaver@ucar.eduBuilding: Room Number: 1001 (Please note location)Host lab/program/group: Type of event: Calendar Timing: Tuesday, June 12, 2018 - 2:00pm to 3:00pm
  • 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: BobbieLast Name: WeaverPhone Extension (4 digits): 8946Email: weaver@ucar.eduBuilding: Room Number: 1022Host lab/program/group: Type of event: Calendar Timing: Thursday, April 12, 2018 - 3:30pm to 4:30pm
  • Antarctica: Catching Snow in the World’s Southernmost Desert

    Snow accumulation is the primary precipitation method that sustains the Antarctic ice sheets. Yet, snowfall remains one of the most difficult meteorological variables to accurately measure in Antarctica. In addition to the harsh, windy conditions that are common during the Antarctic winter, most of Antarctica is considered a desert with estimated yearly snowfall accumulation amounts as low as just two inches in some areas. Because of the challenges associated with measuring snow in these environments, the input (primarily snowfall accumulation) to the mass balance of snow and ice across Antarctica is not well understood. Additionally, the current estimates of precipitation amounts for Antarctica vary significantly, which further complicates our understanding of the mass balance of the ice sheets. A new field program funded by the National Science Foundation is focused on testing recent advances in snowfall measurement technology to determine if accurate snowfall measurements around the McMurdo area are now possible in the Antarctic environment. An overview of the field program will be provided with an emphasis on the recent advances in snowfall measurement. Preliminary data will be shown and the challenges of working in Antarctica and day-to-day life around McMurdo Station will be discussed. (This talk will be a repeat of the recent Explorer Series talk on this topic.)

    Scott Landolt, NCAR
    Tuesday, March 20, 2018

    First Name: JessaLast Name: JohnsonPhone Extension (4 digits): 2751Email: jessaj@ucar.eduBuilding: Room Number: 1022Host lab/program/group: Type of event: Calendar Timing: Tuesday, March 20, 2018 - 10:30am to 11:30am
  • Andy Wood
    Martyn Clark

    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: BobbieLast Name: WeaverPhone Extension (4 digits): 8946Email: weaver@ucar.eduBuilding: Room Number: 1022Host lab/program/group: Type of event: Calendar Timing: Thursday, March 1, 2018 - 3:30pm to 4:30pm
  • The Master Plan of the Shagaya Renewable Energy Park in Kuwait
    Yousuf Al-Abdulla | Kuwait Institute for Scientific Research, Kuwait City, Kuwait

    Kuwait is developing a large (2G) renewable energy facility, including solar and wind, called Shagaya. Kuwait is located on the north end of the Arabian Gulf and experiences the full range of weather conditions through-out the year. The Shagaya facility is located in the desert west of Kuwait city. Several Kuwaitis are visiting NCAR to learn more about renewable power forecasting for the Shagaya facility. This seminar will cover the current plans for renewable energy in Kuwait and highlight areas where they are looking for further collaboration. 

    Yousef Al-Abdullah received a B.S. degree in electrical engineering from Arizona State University, Tempe, in 2007 and a M.S. degree in electrical and computer engineering from the Georgia Institute of Technology, Atlanta, in 2009, and in 2016 he earned his Ph.D. in electrical engineering from ASU. As part of his dissertation he investigated constraint relaxation and out-of-market correction practices in electric energy markets. Upon finishing his degree, he returned to the Kuwait Institute for Scientific Research and is part of the Renewable Energy Program within the Energy and Building Research Center. 

    Tuesday, August 15, 201710:00AMFoothills LabFL2-1022First Name: JessaLast Name: JohnsonPhone Extension (4 digits): 2751Email: jessaj@ucar.eduBuilding: Room Number: 1022Host lab/program/group: Type of event: Calendar Timing: Tuesday, August 15, 2017 - 10:00am to 11:00am
  • Sisi Chen
    Department of Atmospheric and Oceanic Sciences, McGill University
    Montreal, Quebec, Canada

    Shallow convective clouds are ubiquitous, and warm rain largely contributes to the total annual rainfall, particularly in the tropics. Therefore, understanding the microphysical processes inside these cloud systems becomes important. Classical parcel models often produce narrow droplet size distributions (DSDs) which disagree with observations in cumulus clouds. Since the last century, turbulence have been postulated to explain the effective DSD broadening in early cloud stage.

    This work studies the very fundamental process involving droplet condensational and collisional growth to explore the fast warm-rain initiation using the direct numerical simulation (DNS). DNS model can accurately resolve small-scale turbulence and simulates the turbulence impacts on droplets that are tracked in the Lagrangian framework, which is infeasible in other models.

    This is the first modeling study that incorporates both droplet condensational process and collisional process into the DNS model and investigates the full droplet growth history in the turbulent environment. 

    Model results show that condensational growth by itself produces narrow DSD under small-scale turbulence, which is similar to the parcel model results. Results from the simulations that consider pure collision-coalescence process show that small-scale turbulence significantly increases the collision rate between small droplets and thus accelerates the formation of large droplets. In particular, the enhancement is the strongest between similar-sized droplets, which indicates that turbulence effectively broadens the narrow DSD formed by condensational growth. On the other hand, condensational growth considerably brings tiny droplets to 5-10 microns, dynamically shifting the collision rates of those droplets in turbulence. To study how collisional process and condensational process interact under the effect of turbulence, simulation results that consider both condensational and collisional processes will be compared to pure collision-coalescence case. It is shown that the inclusion of condensation significantly changes the behavior of droplet collisions in the turbulence and thus has strong feedback on the DSD broadening. Detailed results and comparison will be presented in the talk.

    Refreshments: 3:15 PM

    First Name: BobbieLast Name: WeaverPhone Extension (4 digits): 8946Email: weaver@ucar.eduBuilding: Room Number: 1022Host lab/program/group: Type of event: Calendar Timing: Thursday, August 17, 2017 - 3:30pm to 4:30pm
  • Observational and modelling-based study of Corsica thunderstorms: preparation of the EXAEDRE airborne campaign

    Eric Defer Laboratoire d'Aérologie, CNRS - Université de Toulouse – OMP – UPS, Toulouse, France The 4-year EXAEDRE (EXploiting new Atmospheric Electricity Data for Research and the Environment; Oct 2016-Sept 2020) project is sponsored by the French Science Foundation ANR (Agence Nationale de la Recherche). This project is a French contribution to the HyMeX (HYdrological cycle in the Mediterranean EXperiment) program. The EXAEDRE activities rely on innovative multi-disciplinary and state of the art instrumentation and modeling tools to provide a comprehensive description of the electrical activity in thunderstorms. The EXAEDRE observational part is based on i) existing lightning records collected during HyMeX Special Observation Period (SOP1; Sept-Nov 2012), and permanent lightning observations provided by the research Lightning Mapping Array SAETTA and the operational Météorage lightning locating systems, ii) additional lightning observations mapped with a new VHF interferometer especially developed within the EXAEDRE project, and iii) a dedicated airborne campaign over Corsica. The modeling part of the EXAEDRE project relies on the electrification and lightning schemes implemented in the French cloud resolving model MesoNH and on Météo-France operational model AROME for innovative investigation of lightning data assimilation.An overview of the EXAEDRE project will be given with an emphasis on both observational and modeling activities performed during the 1st year of the project. The preparation of the EXAEDRE airborne campaign planed in September 2018 over Corsica will then be discussed.Acknowledgements: the EXAEDRE project is sponsored by grant ANR-16-CE04-0005-01 with support from the MISTRALS/HyMeX meta program. Tuesday, August 22, 2017 2:00PM-3:00PM FL2-1001First Name: JessaLast Name: JohnsonPhone Extension (4 digits): 2751Email: jessaj@ucar.eduBuilding: Room Number: 1001Host lab/program/group: Type of event: Calendar Timing: Tuesday, August 22, 2017 - 2:00pm to 3:00pm
  • A Beginners Introduction to the Analog Ensemble Technique


    Have you heard about the Analog Ensemble (AnEn) technique? Would you like to learn more about the technique and its evolution? Want to learn its possible applications and the current state-of-the-art research being conducted using this technique? Then come for a beginners adventure into the AnEn technique brought to you by the Warner Internship for Scientific Enrichment (WISE) Program!

     The Analog Ensemble (AnEn) technique was developed to generate a probability distribution function (PDF) of an expected outcome from a current deterministic forecast and corresponding sets of historical forecasts and verifying observations. The technique has implications in physical science subject areas where: 1) single deterministic predictions, past predictions, and their corresponding observations are available; 2) it is necessary to have quantifiable and justifiable measures of uncertainty; and 3) computational resources are precious.  The AnEn technique provides an alternative option for generating probabilistic forecasts without requiring the computational expense of a NWP ensemble thus allowing scientists to choose between the tradeoff of higher resolution modeling or ensemble modeling at a coarser resolution. The AnEn improves short-term weather prediction accuracy, decreases real-time computational costs, and provides spatial and temporal uncertainty estimation (Delle Monache et al. 2011; Delle Monache et al. 2013; Alessasndrini et al. 2015; Zhang et al. 2015).  Applications of the technique include but are not limited to: a range of weather parameters (e.g., 10-m and 80-m wind speed, 2-m temperature, relative humidity) solar power forecasting, wind power forecasting, air quality forecasting, tropical cyclone predictions, and downscaling of parameters as wind speed and precipitation.  

    Tuesday, July 25, 2017 1:00PM-2:00PM FL2-1001

    First Name: JessaLast Name: JohnsonPhone Extension (4 digits): 2751Email: jessaj@ucar.eduBuilding: Room Number: 1001Host lab/program/group: Calendar Timing: Tuesday, July 25, 2017 - 1:00pm to 2:00pm

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RAL's mission is to conduct directed research that contributes to the fundamental understanding of the atmosphere and related physical, biological, and social systems; to support, enhance, and extend the capabilities of the scientific community, and to develop and transfer knowledge and technology for the betterment of life on Earth.

I have a very strong feeling that science exists to serve human betterment and improve human welfare.       - Walter Orr Roberts

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