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Overview
A Cross-Calibrated Multi-Platform Ocean Surface Wind Velocity Product for Meteorological and Oceanographic Applications
Quikscat and TRMM measurements over hurricane Floyd on September 13, 1999.
This project is funded under the NASA Earth Science Enterprise (ESE) Cooperative Agreement Notice for the Research, Education, Application and Solution Network (REASoN) project, "A Distributed Network of Data and Information Providers For Earth Science Enterprise Science, Applications and Education" (CAN-02-OES-01). In collaboration with private and government institutions, this project seeks to create a cross-calibrated, multi-platform, multi-instrument ocean surface wind velocity data set , for the period extending from 1987 through 2007, with wide ranging research applications in meteorology and oceanography. It represents a continuation and expansion of the SSM/I surface wind velocity data set that we began under the NASA Pathfinder Program. Data derived from SSM/I, AMSRE, TRMM TMI, Quikscat and other missions are combined using a variational analysis to produce a consistent climatological record of ocean surface winds at 25km resolution.
Prior to the launch of satellites capable of determining surface wind from space, observations of surface wind velocity were provided primarily by ships and buoys. While these observations are extremely useful, they also have severe limitations and are generally not adequate for global applications. For example, reports of surface wind by ships are often of poor accuracy, cover only very limited regions of the world's oceans, and occur at irregular intervals in time and space. Buoys, while of higher accuracy, have extremely sparse coverage. Due to these deficiencies, analyses of surface wind that do not include space-based data can misrepresent atmospheric flow over large regions of the global oceans, and this contributes to the poor calculation of wind stress and sensible and latent heat fluxes in these regions.
In response to the wind blowing across it, the ocean surface responds on many wavelengths. This response provides a mechanism for the microwave remote sensing of ocean surface wind from space. The active sensing of the radar backscatter of centimeter-scale capillary waves allows the retrieval of ocean surface wind vectors with some directional ambiguity. The Seasat, ERS and NSCAT scatterometers were designed to take advantage of this phenomenon, but the time periods for which scatterometer data are available are very limited, and not sufficient for studies of inter-annual variability and climate change. Seasat data are available for only the third quarter of 1978 (Atlas et al., 1987). ERS scatterometer data, with more limited coverage, are available from 1992 to the present. The NASA scatterometer (NSCAT) provided data from Fall 1996 through Spring 1997. The latest scatterometer, SeaWinds on Quikscat, was launched in June 1999 and was joined by SeaWinds on ADEOS2 in December of 2002.
Passive microwave remote sensing of the ocean surface also has the capability of retrieving ocean surface winds through the response of the microwave emissivity to the surface roughness (Wentz et al., 1986). Four passive instruments, the Scanning Multichannel Microwave Radiometer (SMMR), the Special Sensor Microwave Imager (SSM/I), the TRMM Microwave Imager (TMI), and the Advanced Microwave Scanning Radiometer ( AMSR) have provided ocean surface wind speed data. SSM/I provides the longest and most continuous record of satellite surface wind observations over the oceans. The major limitation of the SSM/I, as well as TMI and AMSR, has been the lack of wind directions in these data. In an effort to make the SSM/I data more generally useful, we developed several different approaches (ranging from simple direction assignment methods to a variational analysis method) to convert the SSM/I speeds to vector winds and assimilate them into global atmospheric models (Atlas et al., 1983,1987,1991). These approaches were tested using simulated data, SASS winds (with the directional information witheld), and finally with actual SSM/I observations. This evaluation (Atlas and Bloom, 1989) showed the variational analysis method (VAM) to be the most accurate and this is the method that we have used for the processing of SSM/I wind vectors, and will continue to use for the assimilation and processing of SSM/I, TMI, AMSR, and scatterometer surface wind vectors for this project. Using this approach, we assimilate different types of data in a filtering procedure. The resulting analysis is used to assign directions to the passive microwave winds.
Overview
- NASA Official: Michael S. Seablom
- Project Manager: Joseph V. Ardizzone
- Website Manager: Lara Clemence