Numerical Modeling
Atmospheric scientists build numerical models to solve the equations that describe the spate-temporal evolution of the atmosphere quantitatively. The best known examples for such models are numerical weather prediction (NWP) models that have been the backbone of operational weather prediction since the 1980s. Global circulation models (GCMs) coupled with numerical models of the ocean, and sometimes with other components of the Earth system, are used to simulate and predict changes in the climate. GCMs are also used to study the dynamics of other planetary atmospheres, such as those of Mars or Titan. Some models have capabilities to simulate and predict changes in the chemical composition of the atmosphere in addition to the changes in its physical state. Simplified or idealized models of the atmosphere are often used to develop a better understanding of the qualitative behavior of the more complex realistic models.
Faculty
Research Professor
David Bullock Harris Professor of Geosciences
Atmospheric dynamics, stratospheric ozone, climate dynamics, satellite meteorology
Professor
Louis & Elizabeth Scherck Chair in Oceanography
Associate Professor
Mesoscale atmospheric dynamics, topographically forced waves and wakes, numerical modeling and scientific computation
Professor
Hurricanes, moist convection, large-scale dynamics, climate dynamics, climate variability, past climates
Associate Professor
Midlatitude convective storms, particularly supercell dynamics, storm/environment interactions, and probabilistic severe weather forecasting
Professor Emeritus, Research Scientist
Large-scale flow organization and transport, theory of geophysical models, pattern-forming PDEs, numerical simulation of electromagnetic scattering
Professor
Department Head
Variability and predictability of climate on seasonal to millennial timescales, coupled ocean-atmosphere interaction, large-scale dynamics of the atmosphere and the oceans
Professor, Graduate Committee Chair
Atmospheric dynamics, predictability, numerical weather prediction, data assimilation, machine learning
