With the tremendous growth in the numbers and types of observations, and the increasing sophistication of atmospheric models, it is imperative to develop techniques that make optimal use of both. Research at Climate & Space involves both the development of new data assimilation techniques, as well as use of proven statistical methods. We evaluate models, produce optimally merged model and observation datasets, and prepare for future satellite platforms.
One particular research area in Climate & Space and the U-M Department of Civil & Environmental Engineering is geostatistical inverse modeling and geostatistical sampling design. The latter provides optimal spatial and temporal information about the distribution of measured air pollutants or trace gases and helps investigate the Earth’s carbon cycle.