The following CIMMS Research Scientists have agreed to mentor applicants for the Peter Lamb Postdoctoral Fellowship. Please click on their name to find out about their research and contact information.
Alex Fierro, Research Scientist, 120 David L. Boren Blvd., Norman, OK 73072, email@example.com
Background: Lightning and radar data assimilation, storm electrification modeling (including tropical cyclones), tropical convection and climate variability.
Projects: There are two separate projects for a potential post doc:
(i) Real-time data assimilation (DA) methods used in NWP models at the cloud scale have proven critical for decision making during short-term (~ 6 h) forecasts of high-impact weather events. Radar DA, however, suffers from limitations when storms evolve in regions with poor or no coverage by the radar network such as mountainous terrain or over oceans. The DA of high-temporal resolution, total lightning data from a ground-based network (e.g., Earth Networks; ENTLN) or from the Geostationary Lightning Mapper (GLM) can help fill this gap. Initial development and testing of lightning DA (LDA) methods involving nudging (e.g., Fierro et al. 2012, 2014; 2015), 3DVAR (Fierro et al. 2016) and EnKF (Mansell 2014; Allen et al. 2016) techniques in cloud-resolving models suggest that assimilating GLM data would produce improvements in forecasts comparable to those from assimilating radar data. Current work on assimilating lightning data by NSSL and CIMMS adapts the abovementioned previous techniques to make them compatible with NWP models used by the NWS operational centers. A major effort focuses on developing, improving, and evaluating the variational LDA method of Fierro et al. (2016) within the framework of the Gridpoint Statistical Interpolation (GSI) system and within NSSL’s 3DVAR prediction system (NEWS3DVAR, Gao et al. 2013) for a quasi-operational test. Next Spring, a series of quasi real-time 24-h forecasts at convection-allowing scale (dx=3-4 km) from initial conditions created by the LDA algorithm implemented within NEWS3DVAR (coupled with WRF-ARW) will be performed over a regional domain within the eastern 2/3rd of CONUS. The resulting forecasts will generate a large amount of model data for post processing. I would thus be very interested in working with a postdoc (familiar with model IO analysis) to quantify and gauge the impact of the LDA through standard domain-wide, bulk forecast metrics for selected accumulated precipitation fields (APCP, Fierro et al. 2015) or reflectivity thresholds and influence radii, such as the frequency biases, equitable threat scores and fraction skill scores. Other techniques such as Rank histograms (Hamill 2001) could also be envisaged.
(ii) The second project is a follow up to a recently submitted study to JAS wherein the vast amount of data provided by the GOES-16/R NOAA/NASA satellite (ABI and GLM) coupled with additional datasets (satellite or ground based) are conjointly investigated in detail for selected N. Atlantic (EPAC) hurricanes during the 2017 and forthcoming Atlantic (EPAC) seasons. Emphasis should be directed in establishing detailed functional relationships/linkages between storm structure, convective state, intensity metrics and lightning metrics.
Kim Klockow-McClain, Societal Applications Coordinator and Research Scientist, 120 David L. Boren Blvd, Norman, OK 73072, firstname.lastname@example.org, 405-325-0805
Fields of Interest: Risk perception, natural hazards response, warning systems effectiveness, communication of forecast uncertainty
Description of Available Projects: Dr. Klockow-McClain, as part of the Forecasting a Continuum of Environmental Threats (FACETs) program, has begun to develop a program of research focused on the communication of forecast uncertainty. As emerging technologies continue to offer new ways to estimate the likelihood of different futures, including at CIMMS/NSSL, new challenges and opportunities are also emerging to assure this information is conveyed in ways that are useful, usable, and used. Borrowing from techniques used in experimental psychology to explore similar problems, Dr. Klockow-McClain approaches this problem in the spatio-temporal context of hazard warnings – a cutting-edge application in experimental psychology, and among the top-rated research needs identified by NSF’s DRMS directorate.
This research opportunity will focus on public-side understanding and use of probabilistic information, especially from probabilities that are continually updating. Exploring this problem requires the use of cutting-edge experimental approaches, and can include tracking of eye movements, decision experiments, or real-time observation in a testbed environment. A postdoctoral research fellow is sought to help build this body of research, and especially to assist with the development of a large volume of small experiments that can be deployed with low-cost, convenience populations, building an early foundation upon which larger funding opportunities can be pursued from NSF or NOAA.
The ideal candidate would possess a social scientific background in environmental and/or natural hazards topics, with expertise in the development and execution of human subjects experiments. The ultimate goal of this work is to assure that impacts of cutting-edge developments in the physical sciences are optimized through the application of evidence-based communication strategies.
Kim Klockow-McClain and Randy Peppler
Kim Klockow-McClain, Societal Applications Coordinator and Research Scientist, 120 David L. Boren Blvd, Norman, OK 73072, email@example.com, 405-325-0805; and Randy Peppler, Associate Director, firstname.lastname@example.org, 405-325-6667
Fields of Interest: Risk perception, natural hazards response, place attachment, environmental perception & behavioral geography
Description of Available Projects: Drs. Klockow and Peppler have developed a series of studies designed to explore place-based perceptions of tornado risk. Klockow, Peppler and McPherson (2014) describe this phenomenon, which initially emerged from interviews with survivors of the 27 April 2011 tornado outbreak in the Southeastern United States. Their research found that while most people received tornado warnings and understood the gravity of the situation, they carried with them conflicting a priori notions about local tornado threats, including hyper-local climatologies developed through experience, which influenced their perceptions of personal risk as the tornadoes approached. These notions mostly acted to give individuals false confidence that they would not be hit directly by a tornado. While some of the perceptions were found ubiquitous throughout the population (protective nature of hills), others were strongly correlated to particular places or nearby geographic features (cutting a new interstate highway through a previously hilly, wooded landscape). The research by Klockow, Peppler and McPherson (2014) resulted in a categorization for potential reasons why people hold such false confidence including place attachment, local (vernacular) knowledge construction, optimism bias, and the social amplification of risk. Klockow and Peppler have expanded this research into Oklahoma, and through town halls and surveys, they have identified towns and regions within Central Oklahoma where local people feel less risk-prone than the observed tornado climatology would suggest. Their research has also found that low perceptions of risk are connected to lower rates of preparatory actions during tornado watches (Peppler, Klockow and Smith 2017).
Opportunities with this research group include analyses of two survey datasets, including one large survey that experiments with a place attachment measure. A researcher is sought to explore this and other potential measures that can explain the observed heterogeneity in place-based risk perceptions, as well as to identify and develop mitigation strategies in places where residents have low perceptions of risk. The ideal candidate would possess a social scientific background in environmental and/or natural hazards topics, and expertise in surveying and/or GIS analysis. The ultimate goal is to improve societal resiliency to natural hazards through improved understanding and dialog between the meteorological community and the local communities they serve.
Heather Reeves, Research Scientist, OU/CIMMS and NOAA/NSSL, 120 David L. Boren Blvd, Norman, OK, 73072, email@example.com, 405-325-6287
CIMMS Division: National Severe Storms Laboratory, Warning Research and Development Division
Fields of Interest: Winter weather, ground transportation, aviation weather, mountain meteorology
Description of Available Projects: Dr. Reeves is the science lead for the transportation branch of the Multi-Radar/Multi-Sensor (MRMS) system. Her work centers around the development of decision-support tools for aviation and ground transportation and involves the integration of radar observations with other networked-data, including numerical model output. Current projects Dr. Reeves is involved with include a new hydrometeor classification algorithm, a road-hazards detection system, inclusion of novel radar data within the national radar mosaic, and new tools to determine the echo age in the national radar mosaic. Dr. Reeves also has several years’ experience in dynamics of orographic precipitation and in numerical weather prediction. Dr. Reeves is interested in collaborating on any project that can be used to improve safety and efficiency in the transportation sector, particularly those that deal with the detection of unusual forms of weather.
Erik Rasmussen, Senior Research Scientist and Program Manager, 120 David L. Boren Blvd, Norman, OK 73072, firstname.lastname@example.org, 405-325-7837
Dr. Rasmussen has been involved with field observational research for nearly 40 years. Much of this research has pertained to processes related to tornadogenesis, including leadership roles in VORTEX94-95, VORTEX2, VORTEX-SE, and the recent RiVorS project. This research emphasizes the synthesis of as many observational data sets as possible to investigate specific hypotheses.
The research opportunity with Dr. Rasmussen will involve investigation of issues related to how supercells generate, amplify, and reorient three-dimensional vorticity leading to tornado formation. Special emphasis is on the region generally to the left of the potential tornado from which the so-called “vorticity river” emanates, and how low-level updraft intensity and kinematics associated with the rear-flank downdraft can allow/prevent left-flank vorticity from becoming involved in a tornadogenesis process. Observations include mobile mesonet observations of state variables and wind, low-level single and/or multi-Doppler data, and perhaps other novel observations in the near future. The investigations will involve observation synthesis and idealized simulations and theory especially aimed at elucidating dynamical processes.
Alexander Ryzhkov, Senior Research Scientist, 120 David L Boren Blvd., Norman, OK, 73072; Alexander.Ryzhkov@ou.edu, 405-325-6624
Fields of Interest: Investigations of microphysical processes in clouds and precipitation using the data collected by dual-polarization radars, satellite, and lightning sensors combined with in situ observations.
Description of Available Projects: Dr. Ryzhkov’s research is focused on the use of dual-polarization weather radars for understanding the microphysical processes in clouds and precipitation and their appropriate parameterizations in the numerical weather prediction models. The objective of the suggested project is to explore and validate the polarimetric radar microphysical retrieval techniques in rain and ice while complementing them with the information from the satellite and lightning sensors. The retrieval results will be tested using in situ aircraft data wherever available and provided to the cloud modelers for their interpretation and utilization for the improvement of microphysics in the existing models. Of primary importance are the processes of ice formation and their response to the aerosol impact.
The polarimetric radar data available for research include the data collected by operational WSR-88D radars and shorter-wavelength DOE ARM radars with polarization diversity during their field deployments. The radar retrievals should be complemented by the satellite data from recently launched Suomi NPP and GOES-R satellites and the lightning data from the NLDN and LMA lightning sensors.
The project implies close collaboration with the research group at the Hebrew University of Jerusalem on the issues of satellite retrievals and sophisticated cloud modeling. Additional interactions are anticipated with the cloud modelers at the National Severe Storms Laboratory and modeling teams in DOE and NASA.
Ben Schenkel, Research Scientist, University of Oklahoma, 120 David L. Boren Blvd, Suite 5648, Norman, OK, 73072; email@example.com
Fields of Interest: Structure and energetics of polar lows, evaluating polar low fidelity within numerical models
Description of Available Projects: Ben’s research interests generally include improving our understanding of polar low dynamics. Ben has recently begun a collaborative project in the School of Meteorology using both newly available, high-resolution atmospheric reanalyses and cloud-resolving mesoscale modeling to provide a climatological analysis of both polar low structure (e.g., warm core versus cold core) and energetics (e.g., surface fluxes versus quasigeostrophic dynamics). To serve as a foundation for this research, his research will also quantify the fidelity of reanalysis polar low structure using observations. Ben is also more generally interested in quantifying how, if at all, polar lows impact their environment (e.g., Arctic sea ice depletion). Postdoctoral research projects broadly involved with polar lows are welcome, especially in the topics discussed above.
Nusrat Yussouf, Research Scientist, 120 David L. Boren Blvd. Norman, OK 73072, Nusrat_Yussouf@ou.edu, 405-325-6202
Fields of Interest: Probabilistic storm-scale numerical weather prediction (NWP) modeling of high impact weather, including data assimilation, verification, observation impact and ensemble design.
Description of Available Projects:
Dr. Yussouf’s research is on development of a storm-scale ensemble prediction system with the goal to enable next-generation severe storm warning capabilities referred to as Warn-on-Forecast to support United States’ vision for a Weather Ready Nation. Her work involves enhancement of a frequently updated, regional-scale, on-demand, probabilistic, high-resolution (~0.5-1 km) ensemble data assimilation and prediction system for high impact weather events, including extreme rainfall, flash floods, tornado outbreaks, major aviation disruptions due to severe convective thunderstorms and land falling tropical cyclones. She is developing capabilities that can be incorporated into the operational National Weather Service (NWS) storm-scale ensemble data assimilation and forecast systems. Research opportunities exist in one or more of the following topics:
1. Development and testing of storm-scale ensemble-based data assimilation (e.g. ensemble Kalman filter, hybrid ensemble-variational, etc.) techniques for probabilistic short-range forecasts of multi-hazard events.
2. Improve probabilistic flash flood forecasts using a coupled storm-scale atmospheric and distributed hydrological ensemble framework.
3. Identification of model resolution required to predict probabilistic properties of storm characteristics, such as low-level rotations, flash flood producing extreme rainfall, damaging winds and large hail.
4. Improvement of storm-scale weather prediction by reducing errors particularly in the planetary boundary layer and model microphysical schemes through advancement of improved assimilation of new/existing observing capabilities or data sets that are not currently operational at NWS.
5. Improve ensemble reliability and spread by designing/configuring storm-scale ensemble systems using multiphysics and/or stochastic physics techniques.
6. Development of forecast verification techniques and tools for storms based on comparison of forecasts with analyses and observational data sets.