Publications

  1. Akinsola, A. A., G. J. Kooperman, W. M. Hannah, K. A. Reed, A. G. Pendergrass, and W.-C. Hsu, 2023: Evaluation of present-day extreme precipitation over the United States: an inter-comparison of convection and dynamic permitting configurations of E3SMv1, Env. Res. Lett., accepted.
  2. Hsu, W.-C., G. J. Kooperman, W. M. Hannah, K. A. Reed, A. A. Akinsanola, A. Pendergrass, 2023: Evaluating Mesoscale Convection Systems Over the US in Conventional and Multiscale Modeling Framework Configurations of E3SMv1. J. Geo. Res., 128, e2023JD038740.
  3. Lee, J. M., C. Tao, W. M. Hannah, S. Xie, and D. C. Bader, 2023: Assessment of warm and dry bias over ARM SGP site in E3SMv2 and E3SM-MMF,. J. Atmos. Sci., accepted.
  4. Yu, S., Hannah, W. M., Peng, L., Bhouri, M. A., Gupta, R., Lin, J., … Khairoutdinov, M. … & Pritchard, M. S. (2023). ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators. arXiv preprint arXiv:2306.08754.
  5. Liu, N., Pritchard, M. S., Jenney, A. M., & Hannah, W. M. (2023). Understanding precipitation bias sensitivities in E3SM-multi-scale modeling framework from a dilution framework, J. Adv. Model. Earth Sys., 15, e2022MS003460.
  6. Reed, K. A., Stansfield, A. M., Hsu, W.-C., Kooperman, G. J., Akinsanola, A. A., Hannah, W. M., et al. (2023). Evaluating the simulation of CONUS precipitation by storm type in E3SM. Geophysical Research Letters, 50, e2022GL102409.
  7. Golaz, J.-C., Van Roekel, L. P., Zheng, X., Roberts, A. F., Wolfe, J. D., Lin, W., et al. (2022). The DOE E3SM Model version 2: Overview of the physical model and initial model evaluation, J. Adv. Model. Earth Sys., 14, e2022MS003156, https://doi.org/10.1029/2022MS003156.
  8. Yang, Q., W. M. Hannah, & L. R. Leung, 2022: Convective momentum transport and its impact on the Madden-Julian Oscillation in E3SM-MMF, J. Adv. Model. Earth Sys., 14, e2022MS003206.
  9. Giorgetta, M. A., Sawyer, W., Lapillonne, X., Adamidis, P., Alexeev, D., Clément, V., Dietlicher, R., Engels, J. F., Esch, M., Franke, H., Frauen, C., Hannah, W. M., Hillman, B. R., Kornblueh, L., Marti, P., Norman, M. R., Pincus, R., Rast, S., Reinert, D., Schnur, R., Schulzweida, U., and Stevens, B., 2022: The ICON-A model for direct QBO simulations on GPUs, Geosci. Model Dev., 15, 6985–7016.
  10. Hannah, W. M., K. G. Pressel, 2022: Transporting CRM Variance in a Multiscale Modelling Framework, Geosci. Model Dev., 15, 8999–9013.
  11. Hannah, W. M., K. G. Pressel, M. Ovchinnikov, G. S. Elsaesser, 2022: Checkerboard Patterns in E3SMv2 and E3SM-MMFv2, Geosci. Model Dev., 15, 6243–6257.
  12. Peng, L., M. S. Pritchard, W. M. Hannah, P. N. Blossey, C. S. Bretherton, 2022: Load-balancing intense physics calculations to embed regionalized high-resolution cloud resolving models in the E3SM and CESM climate models, J. Adv. Model. Earth Sys., 14, e2021MS002841.
  13. Kooperman, G. J., A. A. Akinsanola, W. M. Hannah, A. G. Pendergrass, & K. A. Reed, 2022: Assessing two approaches for enhancing the range of simulated scales in the E3SMv1 and the impact on the character of hourly US precipitation. Geophysical Research Letters, 49, e2021GL096717.
  14. Hannah, W. M., A. M. Bradley, O. Guba, Q. Tang, J.-C. Golaz, and J. Wolfe, 2021: Separating Physics and Dynamics grids for Improved Computational Efficiency in Spectral Element Earth System Models. J. Adv. Model. Earth Sys., 13, e2020MS002419.
  15. Norman. M. R., D. Bader, C. Eldred, W. M. Hannah, B. Hillman, C. R. Jones, J. M. Lee, L. R. Leung, I. Lyngaas, K. G. Pressel, S. Sreepathi, M. A. Taylor, and X. Yuan, 2020: Unprecedented Cloud Resolution in a GPU-Enabled Full-Physics Atmospheric Climate Simulation on OLCF’s Summit Supercomputer, Int. J. of High Perf. Comp. App., doi:10.1177/10943420211027539.
  16. Akintomide, A., G. Kooperman, K. Reed, A. Pendergrass, W. M. Hannah, 2020: Projected changes in seasonal precipitation extremes over the United States in CMIP6 simulations, Env. Res. Lett., 15, 104078.
  17. Akintomide, A., G. Kooperman, A. Pendergrass, W. M. Hannah, K. Reed, 2020: Seasonal representation of extreme precipitation indices over the United States in CMIP6 present-day simulations, Env. Res. Lett., 15, 094003.
  18. Morrison, H., J. M. Peters, A. C. Varble, S. Giangrande, W. M. Hannah, 2020: Thermal chains and entrainment in cumulus updrafts, Part 2: Analysis of idealized simulations. J. Atmos. Sci., 77, 3661-3681.
  19. Morrison, H., J. M. Peters, A. C. Varble, S. Giangrande, W. M. Hannah, 2020: Thermal chains and entrainment in cumulus updrafts, Part 1: Theoretical description. J. Atmos. Sci., 77, 3637-3660.
  20. Hannah, W. M., C. R. Jones, B. R. Hillman, M. R. Norman, M. A. Taylor, D. A. Bader, L. R. Leung, M. S. Pritchard, M. D. Branson, G. Lin, K. G. Pressel, and J. M. Lee, 2020: Initial Results from the Super-Parameterized E3SM. J. Adv. Model. Earth Sys., 12, e2019MS001863.
  21. Peters, J. M., W. M. Hannah, H. Morrison, 2019: The influence of vertical shear on moist thermals. J. Atmos. Sci., 76, 1645–1659.
  22. Zeng, X., D. Klocke, B.J. Shipway, M.S. Singh, I. Sandu, W. M. Hannah, P. Bogenschutz, Y. Zhang, H. Morrison, M. Pritchard, and C. Rio, 2018: Future Community Efforts in Understanding and Modeling Atmospheric Processes. Bull. Amer. Meteor. Soc., 99, ES159–ES162.
  23. Mapes,B. E., E. S. Chung, W. M. Hannah, H. Masunaga, A. J. Wimmers, C. S. Velden, 2018: The Meandering Margin of the Meteorological Moist Tropics. Geophys. Res. Lett., 45, 1177– 1184.
  24. Singh, M. S., Z. Kuang, E. D. Maloney, W. M. Hannah, B. O. Wolding, 2017: Increasing potential for intense tropical and subtropical thunderstorms under global warming. Proc. Natl. Acad. Sci., 114, 11657–11662.
  25. Hannah, W. M., 2017: Entrainment vs. Dilution in Tropical Deep Convection. J. Atmos. Sci., 74, 3725–3747.
  26. Hannah, W. M., and A. Aiyyer, 2017: Reduced African Easterly Wave Activity with Quadrupled CO2 in the Super-Parameterized CESM. J. Climate, 30, 8253–8274.
  27. Russell, J. O., A. Aiyyer, J. D. White, and W. M. Hannah, 2017: Revisiting the Connection Between African Easterly Waves and Atlantic Tropical Cyclogenesis. Geophys. Res. Lett., 43.
  28. Hannah, W. M., B. E. Mapes, and G. S. Elsaesser, 2016: A Lagrangian View of Moisture Dynamics During DYNAMO. J. Atmos. Sci., 73, 1967-1985.
  29. Hannah, W. M., E. D. Maloney, and M. S. Pritchard, 2015: Consequences of Systematic Model Drift in DYNAMO Hindcasts with SP-CAM and CAM5, J. Adv. Model. Earth Sys., 7, 1051–1074.
  30. Hannah, W. M., and E. D. Maloney, 2014: The moist static energy budget in NCAR CAM5 Hindcasts during DYNAMO, J. Adv. Model. Earth Sys., 6, 420-440.
  31. Hannah, W. M., and E. D. Maloney, 2011: The Role of Moisture-Convection Feedbacks in Simulating the Madden-Julian Oscillation, J. Climate, 24, 2754-2770.
  32. Maloney, E. D., A. H. Sobel, and W. M. Hannah, 2010: Intraseasonal Variability in an Aquaplanet General Circulation Model, J. Adv. Modeling.Earth. Sys, 2, 24 pp.
  33. Matsumoto, H., R. P. Dziak, D. K. Mellinger,M. Fowler, J. Haxel, A. Lau, C. Meinig, J Bumgardner, and W. M. Hannah, 2006: Autonomous Hydrophones at NOAA/OSU and a New Seafloor Sentry System for Real-time Detection of Acoustic Events,
    Oceans’06 MTS/IEEE-Boston, Boston, MA, 18–21 September 2006, 4 pp.

Conference Presentations

2019E3SM All Hands MeetingGrid Imprinting Issues in E3SMoralWestminster, CO
2018Pan-GASS UMAP ConferenceA Super-Parameterized Model for the Exascale Era: Results from the new SP-E3SMoralLorne, Austrailia
2017AGU Fall Meeting Entrainment and Dilution in Tropical Deep ConvectionoralNew Orleans, LA
2016AMS Tropical Entrainment and Dilution in Tropical Deep ConvectionoralSan Juan, PR
2014Research IntersectionsClimate Model Data For Non-Climate ScientistsoralMiami, FL
2014AGU Fall Meeting A Lagrangian View of Moisture-Convection Dynamicsoral & posterSan Francisco, CA
2014AMS Tropical Conference DYNAMO Hindcasts with SP-CAMoral & posterSan Diego, CA
2013Young Scientist Symposium
on Atmospheric Research 		The Moist Static Energy Budget in DYNAMO HindcastsoralFort Collins, CO
2013AGU Fall Meeting The MSE Budget in Hindcast Experiments During DYNAMOposterSan Francisco, CA
2012MJO Workshop posterHonolulu, HI
2012AGU Fall Meeting Vertically Varying Cumulus Entrainment and Convectively Coupled Equatorial Waves in a GCMposterSan Francisco, CA
2012NOAA Climate Diagnostics
and Prediction Workshop 		posterFort Collins, CO
2012Young Scientist Symposium
on Atmospheric Research 		Height Variable Entrainment in a GCMoralFort Collins, CO
2011CMMAP Winter Team Meeting oralBerkeley, CA
2010AMS Tropical Conference The Role of Moisture-Convection Feedbacks in Simulating the MJOoralTucson, AZ

Paper Discussions

Computer Tips / Tricks

Atmospheric Model Info

Notes

PhD Dissertation

Tropical Deep Convection, Entrainment, and Dilution
during the DYNAMO Field Campaign

The bulk of my dissertation was about making the distinction between entrainment and dilution in convection. The word “entrainment” is often used to imply that it is synonymous with dilution. I set out to answer whether this was the case by devising a method for directly measuring dilution. Dilution varies a lot depending on what quantity is being diluted, and this can get very complicated when a quantity is not conserved for moist adiabatic processes. For example, buoyancy and total water will have very different dilution rates for the same rate of mass entrainment.

Masters Thesis

The Role of Moisture-Convection Feedbacks in
Simulating the Intraseasonal Oscillation

My master’s thesis analyzed the sensitivity of the NCAR Community Atmosphere Model (CAM) to varying strength of the Tokioka et al. (1988) minimum entrainment threshold which suppresses deep convection. Increasing this threshold enhances the tropical intraseasonal variability in the model and produces a more coherent MJO as well as a drier and colder mean climate in the model. The Gross Moist Stability (GMS; see Raymond et al. 2009) was also reduced which may be responsible for allowing the model to build up the large-scale moisture anomalies associated with the MJO. Further analysis showed that changes to the time mean GMS is not a reliable metric for diagnosing the model’s ability to simulate a realistic MJO. This is because further CAM simulations which used an alternative method to enhance the MJO did not exhibit this change to the mean GMS. It appears that looking at the intraseasonal fluctuations of GMS provides a better diagnostic for assessing a model’s ability to sustain MJO variability.

Undergraduate Research

Internal Rays of the Mandelbrot Set

My undergraduate thesis focused on mapping the internal structure of the Mandelbrot set (see image below) by projecting a unit disc and the associated internal rays onto the various cardiod and circular bulbs of the Mandelbrot set. The points within these bulbs are associated with a specific period of attracting cycle for a certain connected Julia set defined by some hyperbolic polynomial. I was able to find a relationship between the period of a given bulb and the period of an attached bulb based on the angle of the internal ray which projects onto the connection point. None of this is particularly useful, but it was an interesting project that I really enjoyed working on with my advisor Dave Brown.

SOSUS Hydrophone Data Acquisition System
for Geo-Acoustic Montioring

I did a few internships during my undergraduate days at the Hatfield Marine Science Center (HMSC) with Bob Dziak’s Geo-Acoustics group. The first summer I was there I developed software to record acoustic data from the SOSUS hydrophone array which is operating at the Whidbey Island naval base in Puget Sound.

QUEphone: An Autonomous “Quasi-Eulerian”
Geo-Acoustic Hydrophone

The second summer at HMSC I was able to work on another hydrophone project called the Quasi-Eulerian Hydrophone (QUEphone). The QUEphone is an ARGO float with a hydrophone. This device can control its own buoyancy so that it can move up and down in the water column, which allows near real-time monitoring of acoustic events. Most of the time the float sits on the ocean floor and listens for seismic events. If a significant event occurs the float can pop up to the surface and communicate to the land station via satellite. This has many applications including early tsunami warning, but also offers a cheap alternative to acoustic monitoring with a moored hydrophone array which requires a ship to retrieve the data. When the QUEphone is ascending/descending it gets pushed around by the ocean currents so it can’t be as accurate for locating the source of seismic events as a moored hydrophone array, which is why it’s called Quasi-Eulerian.