The Soil Moisture and Ocean Salinity (SMOS) Ice project explored the potential of retrieving sea-ice information from the SMOS satellite, a polar-orbiting L-band radiometer successfully launched in November 2009. Toward this end, radiance measurements were collected over the Northern Baltic during the Pol-Ice campaign. We test a simple ice-concentration retrieval algorithm on these data and compare the results with ARTIST Sea Ice (ASI) maps derived from the Advanced Microwave Scanning Radiometer on the Earth Observing System. All operational ice-concentration algorithms are based on the same principle which, for the campaign data, reduces to a linear scaling of the radiances because, effectively, only one channel was available. Because of biases introduced by the different footprint sizes of the two radiometers (airborne and satellite), the linear flight path, and pilot selection of preferred surface type, Pol-Ice and ASI concentrations were compared using three different levels of averaging. In the first case, the individual measurements from the airborne radiometer were compared with interpolated ASI values; in the second, they were averaged over the pixels in the ASI maps; and in the third, they were averaged by binning the ASI values in 1% intervals. The correlations were 0.59, 0.67, and 0.76, respectively. Because of the unique operating principle of SMOS, each ground point will be viewed at multiple effective angles within a short time span. It is proposed to exploit this extra information by interpolating to a single effective viewing angle.

Sea ice emissivity models for the L-band frequency range are described and then tested on Pol-Ice campaign field measurements. Pol-Ice was conducted in March 2007 in the Northern Baltic in preparation for the launch of the Soil Moisture and Ocean Salinity (SMOS) instrument, a satellite microwave radiometer operating at 1.4 GHz. The campaign comprised airborne measurements from the EMIRAD L-band radiometer and the E-M Bird ice thickness meter. Because of the translucency of sea ice at L-band, it is hoped that SMOS will render information on ice thickness. Three variations on radiative transfer are used to model EMIRAD brightness temperatures from collocated E-M Bird measurements: a single plane-parallel model, an ensemble of such models, and a ridged Monte Carlo model based on geometric optics that includes both the top- and bottom-surface topography. All three models accurately account for the instrument antenna pattern, relevant to satellite-mounted radiometers that sample a large and heterogeneous area. Because of ice growth processes, salinity and, by extension, permittivity are partial functions of ice thickness; thus, the models are further refined so that permittivity varies with ice thickness, which was necessary to correctly model the polarization difference. Other issues related to effective permittivities, an intermediate quantity in the models, are discussed. Analysis of partial correlations shows that ice ridging makes a significant contribution to the measured signal, commending further study using scattering models that are more appropriate to the scale of the ridging relative to the wavelength.

A methodology for the design and development of data-parallel applications and components is presented. Data-parallelism is a well understood form of parallel computation, yet developing simple applications can involve substantial efforts to express the problem in low level notations. We describe a process of software development for data-parallel applications starting from high level specifications, generating repeated refinements of designs to match different architectural models and performance constraints, enabling a development activity with cost benefit analysis. Primary issues are algorithm choice, correctness, and efficiency, followed by data decomposition, load balancing, and message passing coordination. Development of a data-parallel multitarget tracking application is used as a case study, showing the progression from high to low level refinements. We conclude by describing tool support for the process.