What methods are currently used to compare time series at single points in space with instantaneous but sparsely sampled area averages to “validate” remotely sensed climate data? Are there more sophisticated or advanced methods that could be applied to improve validation tools or uncertainty estimates? Are there alternative means of measuring the same phenomena to confirm the accuracy of satellite observations?
How can fairly short-term, spatially dense remote sensing observations inform climate models operating at long time scales and relatively coarse spatial resolutions? Are there remotely sensed data that could, through the use of modern statistical methods, be useful for improving climate models or informing other types of climate research?
What are the practical and institutional barriers (e.g., lack of qualified statisticians working in the field) to making progress on developing and improving statistical techniques for processing, validating, and analyzing remotely sensed climate data?
In her introductory remarks at the workshop, planning team chair Amy Braverman from the Jet Propulsion Laboratory presented Table 1-1 to illustrate how statistical methods (rows) can help address three major challenges in the use of remotely sensed climate data (the columns). The first of these three major challenges is the validation of remote sensing retrievals. When a remote sensing instrument retrieves a measurement that is used to infer a geophysical value (e.g., atmospheric temperature), uncertainties exist both in the measured values and in the statistical model used to validate the remotely sensed parameter. The second challenge is improving the representation of physical processes within all types of climate models. Workshop participants stressed the need to better represent physical processes within global earth system models, a critical component to projecting future climate accurately, reducing uncertainty, and ultimately aiding policy decisions. The third major challenge in climate research where statistics plays an important role is aggregating the observed and modeled knowledge, each with their associated uncertainties, to develop a better understanding of the climate system that can lead to useful predictions.
Complex and multifaceted relationships in the physics of the climate system contribute uncertainty over and above that which is normally present in making inferences from massive, spatio-temporal data. Isolating and quantifying these uncertainties in the face of multiple scales of spatial and temporal resolution, nonlinear relationships, feedbacks, and varying levels of a priori knowledge poses major challenges to achieving the linkages shown in Table 1-1. A formal statistical model that articulates relationships among both known and unknown quantities of interest and observations can sharpen the picture and make the problem