My group is called the Climate and Extreme Weather Laboratory. We use computer models of the atmosphere as experimental laboratories to understand extreme weather, including hurricanes and tornadoes, and how the climate system produces them. We use what we learn to create better models of these events and their risks. Our work integrates models of real and imaginary worlds with both theory and observations. This work is motivated by both a basic desire to understand how weather works and the need to better understand the risks weather poses to society. Here is a bit of insight into the philosophy behind what I do.
Dan has been awarded an NSF Faculty Early Career Development (CAREER) award! This is a 5-year award grant to study climate controls on the subtropical high and hurricane landfall risk.Some exciting recent papers:
- see more papers below, I'll add more info here soon!
- What happens to the energy of an air parcel as it rises? We demonstrate from first principles how and why an air parcel loses energy as it rises, and how it differs in disorganized vs. organized convection.
- Are we sure the drag coefficient stops increasing at high wind speeds? Maybe not. We revisit the underlying assumptions and data limitations that bring this result into question.
- Can we predict CAPE without lifting an actual parcel? Yes, we show how theoretically, and how it can successfully predict both climatological extreme CAPE and a case study for the April 2011 major tornado outbreak. [led by PhD student Li]
- Can we define a steady environmental sounding supportive of severe convective storms based in the physics of a hydrostatic atmosphere? Yes, we have developed such a theoretical sounding using a static energy framework that has a variety of benefits over the widely-used Weisman and Klemp model.
I’ve made a few short YouTube recorded presentations on content from recent papers, provided next to the citations below (links in blue). Why watch a YouTube video? Because papers are dense and we're all busy. Because videos with pictures and animations are much more visually engaging (fun!) and easier to digest. Because maybe you first want a nice easy introduction to a topic while you eat your lunch before you check out the paper (or don't). Because I often understand my own work better after it's published, so I might explain something better now than I did in the paper itself. Besides, I spend so much time making nice engaging presentations for seminars, I might as well share them publicly!
To go straight to the videos, here's my YouTube playlist of presentations. Recent videos:
- Hurricanes on a thermally-uniform Earth-like aquaplanet (Chavas and Reed 2019, JAS) a) for an Earth hurricane/weather/climate audience and b) for an exoplanet audience;
- Physical understanding of the tropical cyclone wind-pressure relationship (Chavas Reed Knaff 2017, Nat Comm)
- A simple physical model for the tropical cyclone low-level wind field and its variability (Chavas and Lin 2016 and Chavas Lin Emanuel 2015, JAS)
Links for code/data in red below.
My CV (PDF).
Schenkel B. A., Chavas D. R. , Lin N., Vecchi G., Knutson T., and A. Brammer. North Atlantic tropical cyclone outer size and structure remain unchanged by the late 21st century.
Johnson Z., Chavas D. R., and H. Ramsay. Statistical framework for Western Pacific landfall risk through modulation of the Pacific subtropical high and ENSO.
Wang D., Lin Y. , and D. R. Chavas. Tropical cyclone potential size.
Chavas D. R. and F. Li. Bias in U.S. severe convective storm environments is driven by bias in mean state near-surface moist static energy across CMIP6 models. (preprint)
Klotzbach P., Chavas D. R., Bell M. M., Bowen S. G., Gibney E. J., and C. J. Schreck III. Characterizing Continental US Hurricane Risk: Which Intensity Metric is Best? (preprint)
Chen J. and D. R. Chavas. A Model for Predicting the Tropical Cyclone Wind Field in Response to Idealized Landfalls?
Gori A., Lin N., Chavas D. R., and B. Schenkel. North Atlantic tropical cyclone size and storm surge reconstructions from 1950-present.
Lu K. and D. R. Chavas (2022). Tropical cyclone size is strongly limited by the Rhines scale: experiments with a barotropic model. J. Atmos. Sci., Early Online Release. https://doi.org/10.1175/JAS-D-21-0224.1
- What the heck is the Rhines scale? We derive a simple framework to understand how an individual axisymmetric vortex responds to the presence of a planetary vorticity gradient (beta). The framework is based on the Rhines number in the barotropic vorticity equation. This effect is described in terms of a Rhines speed (rather than Rhines scale) that, when given a circulation profile, defines a vortex Rhines scale. The vortex Rhines scale separates an inner region that is quasi-steady from an outer region that stimulates planetary Rossby waves and so decays with time.
- A vortex shrinks with time towards its vortex Rhines scale in barotropic simulation experiments varying either initial vortex size or beta.
- These physics are conceptually consistent with the notion of the Rhines scale as the cutoff of the inverse energy cascade for turbulent baroclinic eddies (extratropical cyclones).
Yang Q., Lee C.-Y., Tippett M. K., Chavas D.R., and T. R. Knutson (2022). Machine Learning based hurricane wind reconstruction. Wea. For., Early Online Release. https://doi.org/10.1175/WAF-D-21-0077.1
Chavas D.R. and J. A.. Knaff (2022). A simple model for predicting the tropical cyclone radius of maximum wind from outer size. Wea. For., Early Online Release. https://doi.org/10.1175/WAF-D-21-0103.1 . (Code for prediction model)
- We provide a simple empirical equation for predicting the radius of maximum wind in a tropical cyclone from routinely-estimated variables: maximum wind speed, storm center latitude, and the radius of 34-kt wind.
- Theory is used to reduce the empirical dependence to two parameters, and then a simple physically-motivated empirical model is fit to historical data to minimize biases (i.e. a ``Human Learning'' (HL) algorithm).
- The model far outperforms existing predictive models and can be easily implemented in operational forecasting, risk models, and research.
Peters J., Mulholland J., and D. R. Chavas (2021). Generalized lapse rate formulas for use in entraining CAPE calculations. J. Atmos. Sci., Early online release.
- We derive a new, general, and more accurate equation for the lapse rate of an air parcel with entrainment.
- A reversible rather than pseudoadiabatic parcel more closely matches real parcels in LES simulations of deep convection.
Chen J. and D. R. Chavas (2021). Can existing theory predict the response of tropical cyclone intensity to idealized landfall?. J. Atmos. Sci., 78(10), pp.3281–3296.
- The time-dependent response of hurricane intensity to drying and roughening combined can be predicted as the product of the responses to each forcing individually.
- Existing intensification theory (an analytic equation) can predict the time-dependent weakening response of an idealized TC for any combination of drying and roughening.
- The theory also compares well with the prevailing empirical decay model, suggesting the theory may be able to predict real the response to real landfalls in nature.
Li F., Chavas D. R., Reed K. A., Rosenbloom N., and D. Dawson II (2021). The role of elevated terrain and the Gulf of Mexico in the production of severe local storm environments over North America. J. Clim., 34(19), pp.7799–7819. [Press: NSF news / Nature World News]
- Severe thunderstorms over North America are strongly dependent on western elevated terrain, but only weakly on the Gulf of Mexico.
- Overturns the long-standing paradigm that the Gulf to the south is crucial to the severe thunderstorm hot spot.
Peters J. and D. R. Chavas (2021). Evaluating the conservation of energy variables in simulations of deep moist convection. J. Atmos. Sci., 78(10), pp.3229–3246.
- We explain the conservation properties of moist static energy theoretically from first principles, including contrasting two specific quantities (MSE-IB and MSE+KE) noted in the literature.
- MSE-IB is very well conserved in in disorganized convection, because it has minimal steady pressure perturbations, whereas MSE-IB and MSE+KE have tradeoffs in their errors for organized forms of convection (squall line, supercell).
- Inclusion of moisture-dependent heat capacities and temperature-dependent latent heats is important for minimizing errors.
Richter D. H., Wainwright C., Stern D., Bryan G., and D. R. Chavas (2021). Potential low bias in high-wind drag coefficient inferred from dropsonde data in hurricanes. J. Atmos. Sci., 78(7), pp.2339–2352.
- The decrease of drag coefficient at high wind speeds is called into question based on uncertainty due to relatively low sample size and a lack of robustness of the flux-profile at high winds.
- In addition, multiple factors may cause an underestimate of the drag coefficient at hurricane-force winds relative to their true values, including uncertainty in the height of recorded dropsonde data, violation of Monin-Obukhov similarity theory near the eyewall, and the short vertical extent of the logarithmic layer
- A quantitative limit may have been reached in using the flux-profile method for this purpose.
Li F. and D. R. Chavas (2021). Midlatitude continental CAPE is predictable from large-scale environmental parameters. Geophys. Res. Lett.. p.e2020GL091799.
- Convective available potential energy (CAPE) can be predicted from environmental sounding parameters without lifting a hypothetical air parcel
- A step-by-step derivation demonstrates how CAPE scales with a recently proposed CAPE-like quantity.
- A simple predictive linear equation is presented based on 20 years of reanalysis data over the U.S.
Chavas D. R. and D. T. Dawson II (2021). An idealized physical model for the severe convective storm environmental sounding. J. Atmos. Sci., 78(2), pp.653-670. [code will be posted soon...]
- A simple two-layer analytic model for the severe convective storm sounding (thermodynamic and kinematic environment) is presented that is phrased in terms of moist static energy.
- To demonstrate its utility, a real-data sounding is fit to the model and experiments are performed introducing a real-data layer and modifying a model parameter.
- Offers a more physically-based alternative to the widely-used Weisman and Klemp sounding model.
Vu T., Kieu C, Chavas D. R., and Q. Wang (2020). A Numerical Study of the Global Formation of Tropical Cyclones. J. Adv. Mod. Earth Sys., p.e2020MS002207.
- Tropical cyclone formation occurs in bi-weekly bursts of approximately 10 TCs in an aquaplanet channel model.
- These bursts are associated with the breakdown of the ITCZ and increase in environmental favorability leading up to breakdown.
- Provides a reasonable back-of-the-envelope explanation for why we have roughly 100 tropical cyclones per year on Earth.
Chavas D. R. and J. Chen (2020). News and Views: Tropical cyclones could last longer after landfall in a warming world. Nature, 587, pp.200-201.
- Discussion of both intrigue and skepticism regarding a recent study that found U.S. landfalling hurricanes are decaying more slowly after landfall.
Ramsay H., Singh M., and D. R. Chavas (2020). Response of tropical cyclone formation and intensification rates to climate warming in idealized simulations. J. Adv. Mod. Earth Sys., 12(10), p.e2020MS002086.
- The maximum intensification rate of a tropical cyclone increases strongly with warming, particularly during the second half of the intensification process, in f-plane radiative-convective equilibrium.
- TCs intensify much more rapidly with increasing temperature than is predicted by extant theory based on potential intensity, suggesting that TCs in a warmer climate may intensify even more rapidly than recent studies suggest.
Alemazkoor N., Rachunok B., Chavas D. R., Staid A., Nateghi R., Tootkaboni M., and A. Louhghalam (2020). Hurricane-induced outage risk under climate change is primarily driven by the uncertainty in projections of future hurricane frequency. Sci. Rep., 10, 15270.
- Future U.S. power outage risk depends strongly on knowledge of future changes in hurricane counts.
- Future projections of hurricane count has a wide range of uncertainty that may be not be reducible anytime soon.
- Power system engineers should consider these limitations in long-term infrastructure planning.
Li F., Chavas D. R., Reed K. A., and D. Dawson II (2020). Climatology of severe local storm environments and synoptic-scale features over North America in ERA5 reanalysis and CAM6 simulation. J. Clim., 33(19), pp.8339–8365.
- ERA5 reanalysis compares very well against radiosondes in reproducing historical environments supportive of severe convective storms over the United States, including at the extremes.
- CAM6 can reproduce the climatological spatial distribution and seasonal cycle of severe convective storm environments, albeit with a high bias in CAPE.
Komacek T., Chavas D. R., and D. Abbot (2020). Hurricane genesis is favorable on terrestrial exoplanets orbiting late-type M dwarf stars. The Astrophysical Journal, 898(2), p.115.
- Hurricanes could exist on terrestrial exoplanets with intermediate rotation in the habitable zones of late-type M dwarf stars.
- Slower rotation does not provide sufficient spin for formation, whereas faster rotation generates greater large-scale wind shear.
Chen J. and D. R. Chavas (2020). The transient responses of an axisymmetric tropical cyclone to instantaneous surface roughening and drying. J. Atmos. Sci., 77(8), pp.2807–2834.
- Hurricane landfall may be thought of first and foremost as a response to surface drying and roughening
- While both forcings ultimately cause a mature hurricane to weaken, the transient responses and physical mechanisms for each differ markedly in both spatial structure and timescale.
Stansfield A. M., Reed K. A., Zarzycki C. M., Ullrich P. A., and D. R. Chavas (2020). Assessing tropical cyclones' contribution to precipitation over the eastern United States and sensitivity to the variable-resolution domain extent. J. Hydromet., 21(7), pp.1425–1445.
O'Neill M. and D. R. Chavas (2020). Inertial waves in axisymmetric tropical cyclones. J. Atmos. Sci., 77(7), pp.2501–2517.
- The size of the axisymmetric outflow anti-cyclone scales very closely with the dry Rossby deformation radius. (But the size of the surface cyclone scales as Vp/f -- see Chavas and Emanuel 2014.)
- The outflow of an axisymmetric hurricane interacts with its environment by generating inertial waves, whose restoring force is the inertial stability of the environment.
- These waves induce thermally-indirect overturning circulations in the outer storm circulation, particularly for high-latitude storms, though they do not penetrate the eyewall and so do not affect the thermodynamic cycle of the strongest winds.
Hoogewind K., Chavas D. R., Schenkel B., and M. O'Neill (2020). Exploring controls on tropical cyclone count through the geography of environmental favorability. J. Clim., 33(5), pp.1725-1745.
- The Earth's thermodynamic environment could allow an order of magnitude more storms than is currently observed each year.
- The regions of the climate system favorable for hurricanes are not densely packed, and thus some other aspect of internal dynamics must limit the annual number of hurricanes globally
Bhalachandran S., Chavas D. R., Marks Jr. F., Dubey S., Shreevastava A., and T.N. Krishnamurti (2020). Characterizing the energetics of vortex-scale and sub-vortex-scale asymmetries during tropical cyclone rapid intensity changes. J. Atmos. Sci., 77(1), pp.315-336. [NOTE: this paper has errors]
Chavas D. R. and K. A. Reed (2019). Dynamical aquaplanet experiments with uniform thermal forcing: system dynamics and implications for tropical cyclone genesis and size. J. Atmos. Sci., 76(8), pp.2257-2274. (I wish I had titled this: "Tropical cyclones on the rotating sphere") YouTube presentation for an Earth hurricane/weather/climate audience and for an exoplanet audience.
- Hurricane genesis rate exhibits a universal quasi-linear increase with the Coriolis parameter.
- Hurricane size in the tropics/subtropics scales with the Rhines scale (not 1/f scale!).
- Minimum hurricane distance from equator scales with equatorial deformation/Rhines scale (not a minimum value of f!).
- Rotating RCE on the f-plane is generalized to the rotating sphere.
- Explains the behavior of convecting vortices on any rotating rocky planet in the absence of any variability in thermodynamic forcing.
- Hurricanes can readily form and persist in dry and/or very cold rotating atmospheres.
- Dry and semidry cyclones have smaller outer radii but similar-sized or larger convective centers compared to moist cyclones, consistent with existing structural theory.
- Spontaneous cyclogenesis fails to occur at moderately low surface wetness values and intermediate surface temperatures of 250–270 K.
Hua Z. and D. R. Chavas (2019). The Empirical Dependence of Tornadogenesis on Elevation Roughness: Historical Record Analysis Using Bayes’ Law in Arkansas. J. Appl. Meteorol. Clim., 58(2), 401-411. Data + MATLAB code provided below
- Tornadogenesis probability increases with increasing elevation roughness, controlling for population density (reporting bias).
- Elevation roughness effect is strongest at smallest roughness length scales of 1 km.
Zhang J., Lin Y., Chavas D. R., and W Mei (2019). Tropical Cyclone Cold Wake Size and Its Applications to Power Dissipation and Ocean Heat Uptake Estimates. Geophys. Res. Lett., 46(16), pp.10177-10185.
Chavas D. R., Reed K. A., and J. A. Knaff (2018). Conference Notebook: Physical Understanding of the Tropical Cyclone Wind-Pressure Relationship. Bull. Amer. Meteo. Soc., 99(12), 2449. (direct PDF link; includes Hurricane Sandy example plot not found in Chavas et al. 2017)
Xian, S., Feng, K., Lin, N., Marsooli, R., Chavas, D., Chen, J. and Hatzikyriakou, A. (2018). Brief communication: Rapid assessment of damaged residential buildings in the Florida Keys after Hurricane Irma. Natural Hazards and Earth System Sciences, 18(7), pp.2041-2045. [Purdue College of Science Insights Magazine article featuring PhD student Jie Chen]
Schenkel, B.A., Lin, N., Chavas, D., Vecchi, G.A., Oppenheimer, M. and Brammer, A. (2018). Lifetime Evolution of Outer Tropical Cyclone Size and Structure as Diagnosed from Reanalysis and Climate Model Data. Journal of Climate, 31(19), 7985-8004.
Lu, P., Lin, N., Emanuel, K., Chavas, D. and Smith, J. (2018). Assessing Hurricane Rainfall Mechanisms Using a Physics-Based Model: Hurricanes Isabel (2003) and Irene (2011). Journal of the Atmospheric Sciences, 75(8), 2337-2358.
McNulty, W. and D. R. Chavas (2018). Covariation of Snowfall Patterns in the Northeastern United States with the Location of the Gulf Stream. The Journal of Purdue Undergraduate Research, 8(1), p.22.
Chavas D. R., Reed K. A., and J. A. Knaff (2017). Physical understanding of the tropical cyclone wind-pressure relationship. Nat. Comm., 8(1360), 1-11. YouTube presentation [USA Today article] [Purdue press article]
Schenkel B., Lin N., Chavas D. R., Oppenheimer M., and A. Brammer (2017). Evaluating outer tropical cyclone size in reanalysis datasets using QuikSCAT Data. J. Clim., 30(21), 8745-8762.
Chavas D. R. (2017). A simple derivation of tropical cyclone ventilation theory and its application to capped surface entropy fluxes. J. Atmos. Sci., 74(9): 2989-2996.
Chavas, D. R. and Lin, N. (2016). A model for the complete radial structure of the tropical cyclone wind field. Part II: Wind field variability. J. Atmos. Sci., 73(8): 3093-3113. YouTube presentation (Model code provided below)
Chavas, D. R., N. Lin, W. Dong, and Y. Lin (2016). Observed tropical cyclone size revisited. J. Clim., 29: 2923–2939.
Hart, R. E., Chavas D. R., and M. P. Guishard (2016). The arbitrary definition of the current Atlantic major hurricane landfall drought. Bull. Amer. Met. Soc., 97(5): 713-722.
Vigh, J. L., E. Gilleland, C. L. Williams, D. R. Chavas, N. M. Dorst, J. M. Done, G. J. Holland, B. G. Brown (2016). A new historical database of tropical cyclone position, intensity, and size parameters optimized for wind risk modeling. Extended Abstract, 32nd AMS Conference on Hurricanes and Tropical Meteorology.
Reed, K. and D. R. Chavas (2015). Uniformly-rotating global radiative-convective equilibrium in the Community Atmosphere Model, version 5. J. Adv. Mod. Earth Sys., 7(4): 1938-1955.
Knutson T. R., J. J. Sirutis, M. Zhao, R. E. Tuleya, M. Bender, G. A. Vecchi, G. Villarini, and D. R. Chavas (2015). Global projections of intense tropical cyclone activity for the late 21st century from dynamical downscaling of CMIP5/RCP4.5 scenarios. J. Clim., 28(18): 7203-7224..
Chavas, D. R, N. Lin, and K. A. Emanuel (2015). A model for the complete radial structure of the tropical cyclone wind field. Part I: Comparison with observed structure. J. Atmos. Sci., 72(9): 3647-3662. YouTube presentation (Model code provided below)
Chavas D. R. and J. Vigh (2014), QSCAT-R: the QuikSCAT tropical cyclone radial structure dataset. NCAR Technical Note, TN-513+STR, 27 pp. (Dataset freely available via NCAR here)
Chavas D. R. and K. A. Emanuel (2014), Equilibrium tropical cyclone size in an idealized state of radiative-convective equilibrium. J. Atmos. Sci., 71(5): 1663-1680.
Elsner J. B., Jagger T. H., Widen H. M., and D. R. Chavas (2014), Daily tornado frequency distributions in the United States. Environ. Res. Lett., 9, 024018, doi:10.1088/1748-9326/9/2/024018.
Chavas D. R., Yonekura E., Karamperidou C., Cavanaugh N., and K. Serafin (2013), U.S. hurricanes and economic damage: extreme value perspective. Nat. Hazards Rev., 14(4), 237–246, doi:10.1061/(ASCE)NH.1527-6996.0000102.
Lin N. and D. R. Chavas (2012), On hurricane parametric wind and applications in storm surge modeling, J. Geophys. Res., 117, D09120, doi:10.1029/2011JD017126.
Dean, L., Emanuel K. A., and D. R. Chavas (2009), On the size distribution of Atlantic tropical cyclones, Geophys. Res. Lett., 36, L14803, doi:10.1029/2009GL039051.
Chavas D. R., Izaurralde R. C., Thomson A. M., and X. Gao (2009), Long-term climate change impacts on agricultural productivity in eastern China, Ag. For. Met., 149(6-7), 1118-1128, doi: 10.1016/j.agrformet.2009.02.001.
Chavas D. R. (2013). PhD Dissertation: "Tropical cyclone size in observations and radiative-convective equilibrium". MIT, Program in Atmospheres, Oceans, and Climate.
Chavas D. R., 2008. "Seasonal climate prediction dissemination to rural farmers in sub-Saharan Africa: a 'bottom-up' perspective and the emergence of the mobile phone". Discussion paper. Internship, World Climate Programme, World Meteorological Organization, Geneva, Switzerland.
Some code/data from prior work. For questions, updates, or bugs, please email me! If you translate code to another language I will gladly post it here.
- QSCAT-R QuikSCAT-based tropical cyclone radial wind profile database (NetCDF): freely available via NCAR here.
- Code for tropical cyclone near-surface wind field model of Chavas et al. (2015, JAS) and Chavas and Lin (2016, JAS). Code is in MATLAB, but also includes a python version (courtesy of Chia-Ying Lee). Ref: Chavas, D. R. (2022). Code for tropical cyclone wind profile model of Chavas et al (2015, JAS). Purdue University Research Repository. doi:10.4231/CZ4P-D448. See Chavas et al. (2015, JAS) and Chavas and Lin (2016, JAS) for complete details, or watch a YouTube presentation.
- Code for Rmax prediction model from Chavas and Knaff (2022, WAF)
- Data + MATLAB code for Hua and Chavas (2019) JAMC paper figures