final_program_abstracts[1]

11 IMSC Session Program
Issues regarding the construction of satellite and in situ
gridded data sets for ocean surface winds and fluxes
Monday - Plenary Session 4
Mark A Bourassa
Dept. of Meteorology & Center for Ocean-Atmospheric Prediction Studies, Florida
State University, Tallahassee, USA
There are remarkable similarities and differences in the statistical problems associated
with creating regularly gridded data sets from satellite and in situ observations. For
applications of a single variable (e,g, wind speed or a vector wind component), the
many of sampling related issues with gridding roughly a day of satellite data are
similar to gridding one month of in situ data for Volunteer Observing Ships (VOS). In
both cases, the sampling is non-homogeneous, with data concentrated in tracks: either
following satellite orbits or major shipping routes). In contract, some gridded products
are products of multiple types of observations (e.g., wind speed, air temperature, sea
surface temperature, and humidity). The observations used to create the gridded
product can be limited to those where all variables are measured simultaneously;
however, this approach greatly reduces the quantity of available data and
consequently increases the gaps in coverage. The quantity of observations can be
increased by combining data gathered at different times (e.g., from different ships or
satellites); however, natural variability and the smoothing of this variability both can
then contribute to substantial errors.
A wide variety of statistical techniques have been employed to fill the gaps in
coverage in a manner that results in a credible product. But what is meant be credible?
These products are typically developed for an in-house application, and then released
for wider use. The qualities desired for one application (e.g., the global energy
balance; minimum bias) are quite different many other applications (e.g., estimating
the depth of the ocean’s mixed layer, which is non-linearly dependent on the wind
speed). Furthermore, the spatial smoothing applied to the data decreases the
resolution. Products are release with clear information on the grid spacing, which
many users confuse with resolution.
Approaches to filling gaps in gridded data will be discussed, with examples of pros
and cons of the techniques. Problems with the estimation of some types of error in
these products will also be demonstrated, as will the impacts on resolution.
Abstracts 31