Prof. Daniel Kirshbaum
Assistant Professor, Ph. D. (Washington)
Office: Burnside Hall 839
Tel: (514) 398-3347
Fax: (514) 398-6115
daniel [dot] kirshbaum [at] mcgill [dot] ca (E-Mail)
Although computer power has increased to the point where operational weather forecasts can explicitly represent large convective storms on the model grid, these forecasts are still plagued by errors in storm timing, location, and precipitation amounts. These errors arise from two dominant sources: (1) uncertainties in the representation of important “subgrid” processes that are unresolved on the model grid, and (2) uncertainties in the initial state of atmosphere, which arise from errors in prior forecasts and incomplete (and uncertain) observations. Due to the non-linear and chaotic nature of the atmospheric fluid-dynamical system, the above errors grow rapidly and ultimately corrupt weather forecasts. My research group aims to improve convective-storm prediction by reducing the amplitude of these errors, accounting for model uncertainties through probabilistic forecasting approaches, and improving the basic physical understanding of convective processes.
To reduce error amplitudes we are studying convective-scale data assimilation, which combines previous model forecasts with high-resolution observations (namely from Doppler radars) to improve the accuracy of the model state. To probabilistically forecast convection events, we use convective-scale ensembles, which sample the probability distribution of different errors sources to yield a spread of forecast outcomes. To improve basic understanding we study the detailed processes controlling the initiation of deep convection and the subsequent organization and life cycle of this convection. This understanding is critical for improving model parameterization schemes that represent the collective effects of subgrid processes and for helping operational forecasters to interpret model forecasts. A focus of this process-based research is the role of complex topography (e.g., mountains, coastlines, urban areas) on cloud initiation and development. To gain insight into these processes, we use a synthesis of observations, cloud-resolving numerical simulations, and explicit-convection simulations with numerical weather prediction models.
Some current projects
- Predictability of deep convection: operational centers are increasingly using convection-permitting models to provide detailed regional forecasts of convection. Do these models succeed in capturing high-impact events? How does the predictability of these events depend on the larger-scale conditions and the type of data assimilation used to initialize these models?
- Representation of mountain convection in large-scale models: global forecasts and climate models lack the resolution to explicitly resolve convective storms, and must rely on cumulus parameterization schemes to represent their effects. However, these schemes are known to suffer from systematic biases in precipitation distribution over mountains. What are the sources of these biases and how do we eliminate them?
- nitiation of deep convection over heated terrain: elevated mountain heating commonly leads to summertime thunderstorms, often in marginally unstable and dry environments that are hostile to deep convection. How do mesoscale circulations, turbulent eddies, and cloud microphysics interact to enable these storms to form?
- The role of mesoscale forcing on cumulus cloud fields: for simplicity cumulus cloud fields are usually studied in horizontally homogeneous environments, but real flows contain mesoscale fluctuations that strongly modify this convection. What impact do these features have on the morphology and internal properties of cumuli?
- Small-scale mechanisms of orographic precipitation enhancement: under what conditions, and by what amount, does convection embedded within orographic clouds enhance the area-averaged precipitation?
- Quasi-stationary convective storms: stationary convection, which often leads to prodigious precipitation amounts and flash-flooding, arises from a number of mechanisms including stationary fronts and forcing from complex topography. Under what environmental conditions do these storms develop, what are their dominant physical mechanisms, and how well are they predicted in weather forecasts?
Some recent publications
Cannon, D. J., D. J. Kirshbaum, and S. L. Gray, 2011: Under what conditions does embedded convection enhance orographic precipitation? Quart. J. Roy. Meteor. Soc., in press, DOI: 10.1002/qj.926.
Hanley, K. E, D. J. Kirshbaum, S. E. Belcher, N. M. Roberts, and G. Leoncini, 2011: Ensemble predictability of an isolated mountain thunderstorm in a high-resolution model. Quart. J. Roy. Meteor. Soc., in press. DOI: 10.1002/qj.877.
Doyle, J. D., S. Gabersek, Q. Jiang, L. Bernardet, J. M. Brown, A. Dornbrack, E. Filaus, V. Grubisic, D. J. Kirshbaum, O. Knoth, S. Koch, J. Schmidli, I. Stiperski, S. Vosper, and S. Zhong, 2011: An intercomparison of T-REX mountain wave simulations and implications for mesoscale predictability. Mon. Wea. Rev., 139, 2811-2831.
Robinson, F. R., S. C. Sherwood, D. Gerstle, C. Liu, and D. J. Kirshbaum, 2011: Exploring the land-ocean contrast in convective vigor using islands. J. Atmos. Sci., 68, 602-618.
Kirshbaum, D. J., 2011: Cloud-resolving simulations of deep convection over a heated mountain. J. Atmos. Sci., 68, 361–378.
Barthlott, C., R. Burton, D. Kirshbaum, K. Hanley, E. Richard, J.-P. Chaboureau, J. Trentmann, B. Kern, H.-S. Bauer, T. Schwitalla, C. Keil, Y. Seity, A. Gadian, A. Blyth, S. Mobbs, C. Flamant, J. Handwerker, 2011: Initiation of deep convection at marginal instability in an ensemble of mesoscale models: A case study from COPS. Quart. J. Roy. Meteor. Soc., 137, 118-136.
Kirshbaum, D. J. and R. B. Smith, 2009: Orographic precipitation in the tropics: large-eddy simulations and theory. J. Atmos. Sci., 66, 2559-2578.
Smith, R. B., P. Schafer, D. J. Kirshbaum, and E. Regina, 2009: Orographic precipitation in the tropics: Experiments in Dominica. J. Atmos. Sci., 66, 1698-1716.
Smith, R. B., P. Schafer, D. J. Kirshbaum, and E. Regina, 2009: Orographic enhancement of precipitation inside Hurricane Dean. J. Hydromet., 10, 820-831.