[1] Simulating land surface hydrological states and fluxes requires a comprehensive set of atmospheric forcing data at consistent temporal and spatial scales. At the continental-toglobal scale, such data are not available except in weather reanalysis products. Unfortunately, reanalysis products are often biased due to errors in the host weather forecast model. This paper explores whether the er...

The lack of adequate tropospheric observations over marine areas adversely affects the forecast skill of numerical weather prediction systems, and can therefore pose a threat to fleet operations. Clearly, space-based remote sensors must be employed to derive high-quality observations to help fill these data voids. In addition, it is important to marry these data with assimilation systems that c...

Remotely-sensed observations from satellites hold great promise for providing a wealth of hightemporal and spatial resolution data, particularly over the oceans. However, it has been very difficult for numerical weather prediction systems to show consistent, positive forecast improvement attributable to satellite retrievals obtained in the conventional manner. By developing techniques for the d...

Remotely-sensed observations from satellites hold great promise for providing a wealth of hightemporal and spatial resolution data, particularly over the oceans. However, it has been very difficult for numerical weather prediction systems to show consistent, positive forecast improvement attributable to satellite retrievals obtained in the conventional manner. By developing techniques for the d...

Two mesoscale numerical weather prediction models, Eta and the Rapid Update Cycle (RUC), are currently being run operationally at the U.S. National Weather Service’s National Meteorological Center to produce nationwide weather forecasts. Improvements in weather forecast accuracy depend on increasing model resolution, which is limited by available computing resources. Massively Parallel Processi...