Techniques to Assimilate SSM/I Observations of Marine Atmospheric Storms
Abstract
Our long-term goal is to use DMSP SSM/I satellite data to improve the depiction of marine atmospheric storms, which in turn would aid nowcasting in support of execution of operations at sea and forecasting of the evolution of these storms in support of planning. The anticipated results of our research are algorithms and prototype software to use SSM/I observations of integrated water vapor (IWV) and surface wind speed in data assimilation. These algorithms, based on the work of Hoffman and Grassotti (1996, referred to as HG96 below), are designed to match observable features in the SSM/I data to those in the short term forecast (or background) field used in the data assimilation system, shifting the background field to best match the available satellite data. The algorithms will be optimized for the Navy weather central regional prediction facility models but will be adaptable to other satellite data and other forecast models. In practical terms, the goal of this project is to translate the work of HG96 into an operational algorithm and to perform sufficient testing to demonstrate the utility of this algorithm by showing that it improves the forecasts of marine storms. To reach this goal we worked towards the following key technical objectives: case selection; impact study; algorithm development and tuning. Suitable cases for study were selected. For each selected case one or more impact experiments was conducted. These experiments showed the utility of applying the distortion correction within the operational data assimilation cycle, and provided quantitative measures of the improvements in forecast skill in the selected cases. The algorithm will be extended to allow for operational implementation. Tuning of the algorithm was refined.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 1998
- Accession Number
- ADA547413
Entities
People
- Ross N. Hoffman
Organizations
- Atmospheric and Environmental Research, Inc