Collaborative Research: Flexible Statistical Models to Blend Massive Geostationary-Derived Climate Data Records

Land surface albedo is the fraction of incoming solar radiation reflected by the land surface. It is an essential climate variable as identified by the Global Climate Observing System. An international effort involving institutions from the USA, European Union, Japan, Switzerland and Korea is dedicated to obtaining geostationary images from five different satellites. Those satellites have overlapping areas for which two different measures of albedo are retrieved. The project will develop blended global albedo products that account for the discrepancies between the different retrievals and quantify the uncertainty in the resulting albedo values using probabilities. The project will leverage the expertise of two PIs to develop state of the art methods that can capture the variability in time and space of climate variables that are observed from a constellation of geostationary satellites, like cloud characteristics, wind speed and direction, snow cover, and precipitation, among others.

The project will contribute geostatistical methods with a model-based approach to analyze, interpolate and make inferences for a multivariate spatial field, featuring a non-stationary spatial process, indexed in a continuous space. The scalability is achieved thanks to a local neighborhood conditioning structure. Computations will be performed fast and concurrently, satisfying global coherence through a hierarchical structure. The model-based nature of the proposed approach allows to account for complex observational errors, perform non-homogeneous multivariate spatial regression, and combine observations obtained at different levels of spatial aggregation. Extensions to spatio-temporal models are seamlessly obtained incorporating space-time interactions within a parsimonious hierarchical model.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.