Research Interests

My group is called the Climate and Extreme Weather Laboratory. We use computer models of the atmosphere as experimental laboratories to understand how hurricanes and severe thunderstorms work and how the climate system produces them. We strive to make what we learn directly useful for real-world forecasting and risk management. This work is motivated by both a basic desire to understand how weather works and the need to reduce its impacts on society. Here is a bit of insight into the philosophy behind what we do. Or check out this Superheroes of Science podcast episode where I talk about what we do for a general audience!

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What problems has our lab solved?

We have worked hard to advance fundamental understanding of hurricanes and severe thunderstorms, and to make these advances directly useful for the prediction of real-world weather and its impacts.

For the hurricane, the surface wind field drives its wind, surge, and rainfall hazards. We developed a simple physics-based model for the surface wind field of a hurricane that reproduces observations and extends to future climates (Chavas et al. 2015, Chavas and Lin 2016), can be applied over land (Chen and Chavas 2023), and can even help explain the existence of dry hurricanes (Cronin and Chavas 2019). This state-of-the-art model is now used widely across the weather risk community to drive hazard models for risk analysis under climate change (Gori et al 2023, Jing et al. 2023). From this physical foundation, we developed a fast empirical model to predict the hurricane radius of maximum wind in real-world operational forecasting, filling a critical gap in forecasting the footprint of the most extreme hazards (Chavas and Knaff 2022; more precise model coefficients in Avenas et al. 2023).

The size of the hurricane wind field has long been poorly understood. We discovered that median hurricane size is controlled by the Rhines scale, a fundamental length scale associated with turbulent fluid flows on a rotating planet (Chavas and Reed 2019; Lu and Chavas 2022). This theory explains why size tends to increase gradually with latitude on Earth (Chavas et al 2015, Chavas and Lin 2016) and why size is not expected to change with warming (Schenkel et al. 2023, Knutson et al. 2015). Moreover, hurricanes rarely form close to the equator, and this theory explains how close they can get -- most of the storm needs to lie on one side of the equator (Chavas and Reed 2019). We then developed a physical model for the maximum potential size on the f-plane (Wang and Chavas 2022) and a physical model for the full time-dependent evolution of size expansion towards its equilibrium size (Wang and Chavas 2024). Ongoing work is extending this model to the sphere and testing its predictions for how expansion rates change under warming across both observations and climate model simulations (Wang et al., in prep). Finally, we showed that hurricane size after landfall (in a simple setting) can be modeled simply as scaling with storm intensity, in line with potential size theory (Chen and Chavas 2023).

We discovered that hurricane genesis rate increases linearly with absolute vorticity up to a peak latitude (Chavas and Reed 2019), corroborating past empirical work on genesis potential indices for which a cap is imposed on the absolute vorticity term.

We discovered how hurricanes work in general in the atmosphere of a rotating sphere like Earth: it intrinsically produces sparse vortices at low-latitudes that drift poleward and westward, similar to the real Earth; and it produces densely packed vortices at high latitudes, identical to the f-plane but very different from Earth. The key driver of this behavior is the Rhines scale, whose effects are significant only at lower latitudes (Chavas and Reed 2019). The Rhines scale was previously applied to explain the size of extratropical cyclones but is now understood to control the size of tropical cyclones too (Lu and Chavas 2022).

We found that hurricanes intensify much more rapidly at warmer temperatures (Ramsay et al. 2020), in line with findings of previous observational and modeling studies. This means that tropical cyclones may become harder to forecast in the future.

Linking physics to risk, we explained for the first time how the minimum pressure at the center of a hurricane depends on both its maximum wind speed and its size (Chavas et al. 2017). We then showed how these physics explain why the minimum pressure is a remarkably good predictor of historical economic damage – much better than maximum wind speed (Klotzbach et al. 2022). Finally, we have developed a very simple predictive model for the minimum pressure from intensity and size for use in operational forecasting (Chavas et al. 2024, submitted). Hence, the minimum pressure is a routinely-estimated quantity that is a simple and effective measure of hurricane damage risk.

For severe thunderstorms, we discovered that “Tornado Alley” over the east-central United States is not driven by the warm waters of the Gulf of Mexico to the south, overturning a decades-long paradigm for why the U.S. is a global hotspot for severe thunderstorms and tornadoes (Li et al. 2020). Instead, we discovered that the smoothness of the Gulf of Mexico ocean surface is a critical driver of North American tornadoes, in contrast to the rough Amazonian landmass that suppresses tornadoes over South America (Li et al. 2023). Additionally, we helped develop the first physical theory for the severe thunderstorm that successfully predicts the intensity of severe thunderstorms simulated from real-world observational data (Peters et al. 2023). This model can account for the effects of future changes in the vertical structure of temperature, relative humidity, and wind shear with warming (Chavas and Li 2023, Davis et al 2023). Ongoing work integrating these advances can explain how severe thunderstorms are expected to increase in intensity with warming (Peters et al., in prep).

Additional topics (narrative TBD): climate controls of tropical cyclogenesis on Earth and on exoplanets (Hoogewind et al. 2020, Vu et al 2020, Komacek et al 2020, Garcia et al. 2024, Chavas et al. 2024 submitted); climate controls of subtropical highs and landfall risk (Johnson et al. 2022, Johnson et al. 2023, Jones et al. 2023); Hurricanes and economic damage (Chavas et al. 2013, Klotzbach et al. 2022)


Videos

Sometimes videos with pictures and animations are much easier to digest than a paper. Sometimes you want a nice easy introduction to a topic while you eat your lunch. Sometimes I can better explain my own results after a paper is published.

My YouTube playlist of presentations. Some recent videos:

Links for code/data in red below.

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CV

My CV (PDF).

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Publications submitted or soon to be...


Klotzbach P., Jones J., Wood K. M., Bell M. M., Blake E. S., Bowen S. G., Caron L.-P., Chavas D. R., Collins J. M., Gibney E. J., Schreck III C. J., Truchelut R. E. The 2023 Atlantic Hurricane Season: An Above-Normal Season Despite Strong El Niño Conditions.

Kruskie A., Chavas D. R., M. Huber. West African heat low climatology and comparison with dry tropical cyclones.

Chavas D. R., Knaff J. A., P. Klotzbach. A simple model for predicting the tropical cyclone minimum central pressure. (preprint)

Chavas D. R., Camargo S., M. Tippett. Tropical cyclone genesis potential using a ventilation-reduced potential intensity. (preprint)

Peters J.M., Chavas D. R., Li F., Lebo Z. J., and C-Y Su. Thunderstorms realize less of their CAPE in a warmer atmosphere.

Lu K.-Y., Chavas D. R., and D. Wang. The persistence of a tropical cyclone seed vortex depends on its structure. (preprint)

Wang Y., Zhu L., Chavas D. R., Johncox M., Yung Y. Leading Role of Saharan Dust on Tropical Cyclone Rainfall in the Atlantic Basin.

Johnson Z. F., Chavas D. R., Jones J. J., Chikamoto Y., and H. A. Ramsay. Impacts of zonal SST gradients on subtropical highs and implications on seasonal tropical cyclone landfall risk.

Jones J. J., Chavas D. R., and Z. F. Johnson. Inter-basin versus intra-basin sea surface temperature forcing of the Western North Pacific subtropical high’s westward extensions. (preprint)

Davis I., Li F., and D. R. Chavas. Future changes in vertical structure of severe convective storm environments over the central Great Plains (preprint).

Li F., Chavas D. R., Medeiros B., Reed K. A., and K. Rasmussen. Upstream surface roughness and terrain is a strong driver of the contrast in tornado potential between North and South America. (preprint)

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Publications by year


2024

Wang D., and D. R. Chavas. An analytical model for tropical cyclone size expansion on the f-plane. J. Atmos. Sci. Early Online Release.

  1. A novel analytic model for tropical cyclone expansion towards its equilibrium (potential) size on the f-plane is derived.
  2. Conceptually, a tropical cyclone enhances total latent heating above and beyond total radiative cooling over the TC volume. This imbalance generates an overturning circulation whose low-level inflow imports angular momentum (absolute vorticity), causing the storm circulation to expand until the system is returned to a radiative-convective equilibrium state.
  3. The theory does pretty well in predicting the size evolution of TCs in idealized axisymmetric simulations over a range of values of potential intensity, Coriolis parameter, and sea surface temperature.

Garcia V., Smith C., Chavas D. R., and T. Komacek (2024). Tropical cyclones on tidally locked rocky planets: Dependence on rotation period. Ap. J. 965(5).

  1. Earth-based environmental parameters, including the ventilated potential intensity derived in Komacek et al. (2020) based on the theory of Tang and Emanuel (2012), successfully predicts the regions of explicitly-simulated tropical cyclone activity in simulations of tidally-locked exoplanets at varying rotation rates.
  2. These results should be applied to Earth, too. [which is done in a forthcoming paper!]

Jing R., Heft-Neal S., Chavas D. R., Griswold M., Wang Z., Clark-Ginsberg A. Guha-Sapir D., Bendavid E., and Z. Wagner (2024). Global Population Profile of Tropical Cyclone Exposure 2002-2019. Nature 626, pp.549–554.

  1. The population exposed to tropical cyclones is increasing, becoming older and are more socioeconomically vulnerable than the non-exposed within a given country.
  2. Consideration of changes in the socioeconomic and health vulnerability of populations exposed to tropical cyclones is a critical piece of the puzzle for evaluating current and future risk of tropical cyclone impacts.

2023

Avenas A., Mouche A., Tandeo P., Piolle J-F, Chavas D., Fablet R., Knaff J., and B. Chapron (2023). Reexamining the Estimation of Tropical Cyclones Radius of Maximum Wind from Outer Size with an Extensive Synthetic Aperture Radar Dataset. Mon. Wea. Rev. 151(12), pp.3169-3189. (Code for Rmax prediction model)

  1. An updated version of the model of Chavas and Knaff (2022, WAF) to predict the tropical cyclone radius of maximum wind, with improved coefficients estimated from synthetic aperture radar (SAR) data -- the current gold standard for near-surface wind speeds over ocean.
  2. CK22 model also extended to R50kt and R64kt (whose values are generally less certain than R34kt in Best Track data, but if you have good data for them then this is viable).

Chavas D. R, and J. Peters (2023). Static energy deserves greater emphasis in the meteorology community. Bulletin of the American Meteorological Society. 104(10), pp.E1918-E1927.

  1. Static energy is widely used in climate science research, but not so much in meteorology research or weather-focused textbooks.
  2. Meteorology scientists and educators would benefit from familiarizing themselves with static energy because of the conceptual benefits it offers for explaining simple concepts in our field.

Schenkel B. A., Noble C., Chavas D. R., Chan K. T. F., Barlow S. J., Singh A., and K. Musgrave (2023). Recent Progress in Research and Forecasting of Tropical Cyclone Outer Size. Tropical Cyclone Res. Rev. 12(3), pp.151-164.

  1. A review of lots of recent advances in tropical cyclone outer size science as part of the IWTC 2022.

Peters J., Chavas D. R., Su C. Y., Morrison H., and B. E. Coffer (2023). An analytic formula for entraining CAPE in mid-latitude storm environments. J. Atmos. Sci. 80(9), pp.2165–2186.

  1. A new theory for entraining CAPE (and peak vertical velocity) in thunderstorms in the presence of large-scale vertical wind shear.
  2. Wind shear produces storm-relative inflow that supports a wider equilibrium plume radius, which reduces the effect of entrainment and hence yields larger ECAPE and stronger vertical velocity
  3. Theory does very well in predicting the peak vertical velocity from simulations initialized by observed soundings from a large severe storm sounding database over the United States.

Peters J. M., Lebo Z. J., Chavas D. R., and C-Y Su (2023). Entrainment makes pollution more likely to weaken deep convective updrafts than invigorate them. Geophysical Research Letters. 50(12), p.e2023GL103314.

  1. Past work arguing that aerosols can make convection stronger did not account for entrainment.
  2. Using a simple entraining plume developed in Peters et al. (2022), we show that when realistic entrainment is included aerosols will reduce parcel buoyancy and thus weaken convection.

Chavas D. R., Liang, J., Baldwin, M., Zhou, M., Rustad, L., McCray, C., DiVito, S., Kartheiser, R. and Mullens, E. (2023). Summary of an interdisciplinary workshop on risk-relevant gaps and needs in freezing rain science. Bulletin of the American Meteorological Society, 104(7), pp.E1265-E1267.

  1. Real-world practitioners worked with atmospheric scientists to determine the lowest hanging fruit for future research in freezing rain science that would actually help practitioners make better decisions to reduce the imapcts of freezing rain.

Gori A., Lin N., Chavas D. R., and B. Schenkel (2023). North Atlantic tropical cyclone size and storm surge reconstructions from 1950-present. JGR-Atmos., p.e2022JD037312.

  1. Storm surge simulations forced with the wind field model of Chavas et al. (2015) with Vmax and track from Best Track and outer size from ERA5 reanalysis does very well in reproducing historical CONUS peak storm surges.

Chen J. and D. R. Chavas (2023). A model for the tropical cyclone wind field response to idealized landfall. J. Atmos. Sci. 80(4), pp.1163–1176.

  1. The size of the tropical cyclone wind field shrinks after landfall following the maximum wind speed in simple axisymmetric landfall model experiments. This outcome aligns with recent theory for physical parameters that govern storm size dynamics.
  2. The radial structure of the tropical cyclone wind field after landfall is reasonably well reproduced by an existing physical model that is already known to work well over ocean.
  3. Our conclusions are expected to apply to the axisymmetric structure of real world storms as well, as our findings do not depend on any arbitrary physical parameters. Though our model cannot account for the spatial heterogeneity of real-world surfaces.

Chavas D. R. and F. Li (2023). Biases in CMIP6 historical U.S. severe convective storm environments driven by biases in mean-state near-surface moist static energy. Geophys. Res. Lett. 49(23), p.e2022GL098527.

  1. Biases in magnitude and spatial structure of the climatology of severe convective storm environments over North America vary widely across CMIP6 models, with a couple of models reproducing both well.
  2. Biases in SCS environments across models are driven principally by biases in mean-state near-surface moist static energy, particularly in Springtime.

Schenkel B. A., Chavas D. R. , Lin N., Vecchi G., Knutson T., and A. Brammer (2023). North Atlantic tropical cyclone outer size and structure remain unchanged by the late 21st century. J. Clim. 36(2), pp.359-382.

  1. Outer storm size does not change with warming in a coherent manner across a range of climate model simulations.
  2. This result is consistent with a Rhines scaling for outer TC size, which is does not depend strongly on temperature.

2022

Klotzbach P., Chavas D. R., Bell M. M., Bowen S. G., Gibney E. J., and C. J. Schreck III (2022). Characterizing Continental US Hurricane Risk: Which Intensity Metric is Best? JGR-Atmos. p.e2022JD037030. EOS Editor's Highlight

  1. The minimum central pressure of a tropical cyclone is inherently an integrated measure of the wind field that is a remarkably good predictor of historical US economic damages. We should consider using it as the basis for our category system to communicate risk.

Wang D., Lin Y. , and D. R. Chavas (2022). Tropical cyclone potential size. J. Atmos. Sci., 79(11), pp.3001-3025.

  1. A physical model for the maximum potential size of a tropical cyclone: a larger storm has a lower central pressure (gradient wind balance), but a larger storm also has less available work to reduce the pressure. There is a unique solution.

Johnson Z., Chavas D. R., and H. Ramsay (2022). Statistical framework for Western Pacific landfall risk through modulation of the Pacific subtropical high and ENSO. J. Clim., 35(22), pp.3787-3800.

  1. A simple framework to model seasonal landfall counts as the product of a poisson model for genesis and a logistic model for conditional landfall probability, with ENSO and subtropical high covariates.

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., 79(8), pp.2109–2124. https://doi.org/10.1175/JAS-D-21-0224.1

  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.
  2. A vortex shrinks with time towards its vortex Rhines scale in barotropic simulation experiments varying either initial vortex size or beta.
  3. 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).

Peters J., Mulholland J., and D. R. Chavas (2022). Generalized lapse rate formulas for use in entraining CAPE calculations. J. Atmos. Sci., 79(3), pp.815-836.

  1. We derive a new, general, and more accurate equation for the lapse rate of an air parcel with entrainment.
  2. A reversible rather than pseudoadiabatic parcel more closely matches real parcels in LES simulations of deep convection.

Yang Q., Lee C.-Y., Tippett M. K., Chavas D.R., and T. R. Knutson (2022). Machine Learning based hurricane wind reconstruction. Wea. For., 37(4), pp.477-493. 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., 37(5), pp.563-579. https://doi.org/10.1175/WAF-D-21-0103.1 . (Code for Rmax prediction model)

  1. 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.
  2. 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).
  3. The model far outperforms existing predictive models and can be easily implemented in operational forecasting, risk models, and research.

2021

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.

  1. 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.
  2. Existing intensification theory (an analytic equation) can predict the time-dependent weakening response of an idealized TC for any combination of drying and roughening.
  3. 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]

  1. Severe thunderstorms over North America are strongly dependent on western elevated terrain, but only weakly on the Gulf of Mexico.
  2. 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.

  1. 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.
  2. 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).
  3. 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.

  1. 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.
  2. 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
  3. 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.

  1. Convective available potential energy (CAPE) can be predicted from environmental sounding parameters without lifting a hypothetical air parcel
  2. A step-by-step derivation demonstrates how CAPE scales with a recently proposed CAPE-like quantity.
  3. 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 for environmental sounding model)

  1. 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.
  2. 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.
  3. Offers a more physically-based alternative to the widely-used Weisman and Klemp sounding model.

2020

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.

  1. Tropical cyclone formation occurs in bi-weekly bursts of approximately 10 TCs in an aquaplanet channel model.
  2. These bursts are associated with the breakdown of the ITCZ and increase in environmental favorability leading up to breakdown.
  3. 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.

  1. 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.

  1. 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.
  2. 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.

  1. Future U.S. power outage risk depends strongly on knowledge of future changes in hurricane counts.
  2. Future projections of hurricane count has a wide range of uncertainty that may be not be reducible anytime soon.
  3. 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.

  1. ERA5 reanalysis compares very well against radiosondes in reproducing historical environments supportive of severe convective storms over the United States, including at the extremes.
  2. 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.

  1. Hurricanes could exist on terrestrial exoplanets with intermediate rotation in the habitable zones of late-type M dwarf stars.
  2. 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.

  1. Hurricane landfall may be thought of first and foremost as a response to surface drying and roughening
  2. 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.

  1. 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.)
  2. The outflow of an axisymmetric hurricane interacts with its environment by generating inertial waves, whose restoring force is the inertial stability of the environment.
  3. 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.

  1. The Earth's thermodynamic environment could allow an order of magnitude more storms than is currently observed each year.
  2. 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. + CORRIGENDUM

2019

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.

  1. Hurricane genesis rate exhibits a universal quasi-linear increase with the Coriolis parameter.
  2. Hurricane size in the tropics/subtropics scales with the Rhines scale (not 1/f scale!).
  3. Minimum hurricane distance from equator scales with equatorial deformation/Rhines scale (not a minimum value of f!).
  4. Rotating RCE on the f-plane is generalized to the rotating sphere.
  5. Explains the behavior of convecting vortices on any rotating rocky planet in the absence of any variability in thermodynamic forcing.

Cronin T. W. and D. R. Chavas (2019). Dry and semi-dry tropical cyclones. J. Atmos. Sci., 76(8), pp.2193-2212. [Space.com article]

  1. Hurricanes can readily form and persist in dry and/or very cold rotating atmospheres.
  2. Dry and semidry cyclones have smaller outer radii but similar-sized or larger convective centers compared to moist cyclones, consistent with existing structural theory.
  3. 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

  1. Tornadogenesis probability increases with increasing elevation roughness, controlling for population density (reporting bias).
  2. 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.

2018

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.

2017

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.

2016

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.

2015

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)

2014

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.

2013

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.

2012

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.

2010

Chavas D. R. and K. A. Emanuel (2010), A QuikSCAT climatology of tropical cyclone size, Geophys. Res. Lett., 37, L18816, doi:10.1029/2010GL044558. Dataset used in analysis is available here.

2009

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.

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Other

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.

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Data/Code

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.

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