Sensitivity of Ocean Color Atmospheric Correction to Uncertainties in Ancillary Data: A Global Analysis With SeaWiFS Data

Global Sensitivity Analysis with 2D Hydraulic Codes: Application on Uncertainties Related to High-Resolution Topographic Data

Technologies such as aerial photogrammetry allow production of 3D topographic data including complex environments such as urban areas. Therefore, it is possible to create High Resolution (HR) Digital Elevation Models (DEM) incorporating thin above ground elements influencing overland flow paths. Even though this category of big data has a high level of accuracy, there are still errors in measur...

Sampling biases in MODIS and SeaWiFS ocean chlorophyll data

Although modern ocean color sensors, such as MODIS and SeaWiFS, are often considered global missions, in reality it takes many days, even months, to sample the ocean surface enough to provide complete global coverage. The irregular temporal sampling of ocean color sensors can produce biases in monthly and annual mean chlorophyll estimates. We quantified the biases due to sampling using data ass...

The continuity of ocean color measurements from SeaWiFS to MODIS

The Ocean Biology Processing Group (OBPG) at NASA’s Goddard Space Flight Center is responsible for the processing and validation of oceanic optical property retrievals from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and the Moderate Resolution Imaging Spectroradiometer (MODIS). A major goal of this activity is the production of a continuous ocean color time-series spanning the mission ...

Global atmospheric forcing data for Arctic ice-ocean modeling

We compare three forcing data sets, all variants of NCEP forcing, in global ice-ocean simulations and evaluate them for use in Arctic model studies. The data sets include the standard Arctic Ocean Model Intercomparison Project (AOMIP) protocol, standard NCEP forcing fields, and the data set of Large and Yeager (2004). We explore their performance in Arctic simulations using a global, coupled, s...

Bayesian Methodology for Atmospheric Correction of PACE Ocean-Color Imagery