Concepts

Light absorption and reflection properties of tissues that vary with chemical composition and structure

The accuracy of predicted values using root mean square error to assess model performance

Statistical assessment of how well model outputs match observational data using metrics like correlation and efficiency measures

Machine learning approach that combines multiple algorithms to improve predictive performance and robustness

Measure of the relative contribution of each predictor variable obtained by calculating mean decrease in mean square error

Radar systems that transmit and receive both horizontal and vertical polarizations to characterize particle shape, orientation, and microphysical properties

Statistical approach using prior distributions and likelihood functions to make probabilistic inferences about model parameters

Methodology for measuring turbulent fluxes using high-frequency measurements of vertical wind velocity and scalar concentrations

Integration of time-series data across different temporal resolutions and scales

Statistical approach using prior information and measurement uncertainties to optimally estimate atmospheric properties

Spatial intersection of radar beams from different systems enabling measurement comparison

Ability of models to predict across different spatial domains and time points using information from multiple locations and times