Our research area includes a wide range of topics in the field of Atmospheric and Space Physics:
Space weather includes any and all conditions and events on the sun, such as solar prominences, flares, coronal mass ejections, high-speed solar wind, and solar energetic particles, in near-Earth space and in our upper atmosphere that can affect space-borne and ground-based technological systems and through these, human life and endeavor.
The ionosphere is the regions of the atmosphere of the earth's atmosphere which contains a large number of electrically charged particles (ions and electrons), ionized by solar and cosmic radiation. The ionosphere acts as a reflecting layer for radio waves on the top and makes the communication possible beyond the line of sight. The extreme ultraviolet from solar irradiance controls the Ionosphere, which is a major source for its variabilities and in the absence of large solar and geomagnetic variation, however, the ionosphere can still exhibit significant variations, which are likely related to disturbances from the lower atmosphere, in the form of gravity waves, planetary waves and tides. The cause of short-term variability of the Ionosphere-Thermosphere system is an important research topic in space environment studies.
Radiative cooling is the process by which a body loses heat by converting thermal energy into radiation. In the mesosphere and lower thermosphere (MLT) region, net heating depends almost exclusively on the imbalance between local absorption of solar ultraviolet radiation and infrared radiative loss. In this region, ozone is the dominant absorber and carbon dioxide is the dominant emitter. Infrared emission by carbon dioxide, ozone, nitric oxide and water vapor play an important role in infrared cooling. The calculation of infrared cooling is not simple, due to uncertainties in the measurements in solar fluxes, cross sections and composition of radiative trace species in the MLT region. The exploration of the detailed mechanisms of significant physical processes related to atmospheric radiation and its interactions with dynamics and chemistry is an important research area.
The middle atmosphere is the region from the tropopause to the homopause. In this part of the atmosphere eddy processes keep the constituents well mixed and ionization plays only a minor role. Carbon dioxide, water vapor and ozone are major trace species with some minor species like nitrous oxide, methane , and the chlorofluoromethanes which play significant roles in the chemistry of the middle atmosphere. Organic halogenated compounds including the industrially manufactured chlorofluorocarbons have become a dominant source of ozone-depleting chlorine and bromine in the stratosphere. Wave motion such as planetary waves, gravity waves, tides and turbulent mixing also play significant role in the middle atmospheric dynamics. The distributions of certain chemical species, particularly ozone, can influence the radiative budget of the atmosphere, affecting temperatures and flow patterns. Therefore the study of atmospheric chemistry intersects greatly with those of fluid dynamics and meteorology.
An aurora also commonly known as the polar lights, is a natural phenomenon of colorful lights display in the sky, predominantly seen in high-latitude regions (around the Arctic and Antarctic). Airglow (also called nightglow) is a faint emission of light by a planetary atmosphere. In the case of Earth's atmosphere, this optical phenomenon causes the night sky never to be completely dark, even after the effects of starlight and diffused sunlight from the far side are removed. Both aurora and airglow are caused by excitation of atmospheric species followed by subsequent radiation of photons. They are, however, quite different in terms of excitation mechanisms, temporal and spatial characteristics, intensity, and dominant emissions.
Predictive modeling is a data-mining solution that helps predicting future outcomes by analyzing historical data and current data. Predictive modeling involves collecting data, formulating a statistical model, predicting, and validating (or revising) that model. Predictive modeling has been around for decades, but only recently was it considered a subset of AI, often linked to machine learning. It’s used to predict the likelihood of specific outcomes based on data collected from similar past and present events.