Earth-observing satellite data provide multi-scale perspectives on atmosphere-ocean systems in both global and regional contexts. Our research exploits the volume and precision of multi-platform data in conjunction with in-situ data and process models for particular phenomena in the earth system. Using methodologies like Bayesian Hierarchical Models (BHMs) and Cyclostationary Empirical Orthogonal Functions (CSEOFs), we test the limits of satellite and in-situ measurements of the atmosphere and ocean. In particular, we seek to develop and implement techniques that use satellite data in novel and innovative ways to obtain a better understanding of the earth system. Sample research includes sea-level reconstructions based on multi-variate historical datasets, regional and global ensemble surface wind dataset construction, ocean forecast model error analyses, Madden-Julian Oscillation studies, and tsunami detection and monitoring using satellite remote sensing.
- "Uncertainty Management in Coupled Physical-Biological Lower Trophic Level Ocean Ecosystem Models"
- "Modeling 3-D spatio-temporal biogeochemical processes with a forest of 1-D statistical emulators."
- "Trends in sunspots and North Atlantic sea level pressure."
- "Ocean ensemble forecasting. Part II: Mediterranean Forecast System response."
- "Dominant spatial variability scales from observations around the Hawaiian Islands."
- "Ocean ensemble forecasting. Part I: Ensemble Mediterranean winds from a Bayesian hierarchical model"
- Updating Advice on Selecting Level-2,3 Surface Vector Wind Datasets: Applications Perspective.
- Multi-Platform Analyses of MJO COnvection of Sub-Daily Timescales.
- Identifyin Loci for Ocean Forecast Model Error from Ensambles of Surface Winds based on QuikSCAT and COAMPS.
- Spatio-Temporal Surface Vector Wind Retrieval Error Models.