Yu, L., & Jin, X. (2014). Confidence and sensitivity study of the OAFlux multisensor synthesis of the global ocean surface vector wind from 1987 onward. J. Geophys. Res. Oceans , 119 (10), 6842–6862.
Paget, A. C., Bourassa, M. A., & Anguelova, M. D. (2015). Comparing in situ and satellite-based parameterizations of oceanic whitecaps. J. Geophys. Res. Oceans , 120 (4), 2826–2843.
Bourassa, M. A., Legler, D. M., O'Brien, J. J., & Smith, S. R. (2003). SeaWinds validation with research vessels. J. Geophys. Res. , 108 (C2).
Hilburn, K. A. (2003). Development of scatterometer-derived surface pressures for the Southern Ocean. J. Geophys. Res. , 108 (C7).
Nyadjro, E. S., Jensen, T. G., Richman, J. G., & Shriver, J. F. (2017). On the Relationship Between Wind, SST, and the Thermocline in the Seychelles-Chagos Thermocline Ridge. IEEE Geosci. Remote Sensing Lett. , 14 (12), 2315–2319.
Bourassa, M. A., & Weissman, D. E. (2003). The development and application of a sea surface stress model function for the QuikSCAT and ADEOS-II SeaWinds scatterometers. In IEEE International Symposium on Geoscience and Remote Sensing (IGARSS) (pp. 239–241).
Dukhovskoy, D., & Bourassa, M. (2011). Comparison of ocean surface wind products in the perspective of ocean modeling of the Nordic Seas. In OCEANS 2011 .
Hoffman, R. N., Privé, N., & Bourassa, M. (2017). Comments on “Reanalyses and Observations: What's the Difference?”. Bull. Amer. Meteor. Soc. , 98 (11), 2455–2459.
Abstract: Are there important differences between reanalysis data and familiar observations and measurements? If so, what are they? This essay evaluates four possible answers that relate to: the role of inference, reliance on forecasts, the need to solve an ill-posed inverse problem, and understanding of errors and uncertainties. The last of these is argued to be most significant. The importance of characterizing uncertainties associated with results—whether those results are observations or measurements, analyses or reanalyses, or forecasts—is emphasized.
Lombardi, K. C. (2004). Resolving the Diurnal and Synoptic Variance of Scatterometer Vector Wind Observations . Master's thesis, Florida State University, Tallahassee, FL.
Abstract: Scatterometer observations of vector winds are used to examine the amplitudes of synoptic and diurnal cycles. Scatterometers have the advantage of providing global coverage over water; however, irregular temporal sampling complicates the analyses. A least squares technique is used in determination of the amplitudes and phases of the diurnal and synoptic cycles on spatial scales of 5°, 15°, and 30°. In open ocean areas and regions with sufficient open water, the magnitudes of the diurnal and synoptic cycles are 1.0 ms-1 and 3.5ms-1, respectively. Diurnal amplitudes are highest in the polar regions and close to land surfaces due to sea breeze effects. The fraction of variance explained by the diurnal cycle is greatest near the equator. Synoptic amplitudes are consistently larger downwind of land from storm tracks and in the southern polar region as the time analyzed is during the southern winter season.
May, J. (2010). Quantifying Variance Due to Temporal and Spatial Difference Between Ship and Satellite Winds . Master's thesis, Florida State University, Tallahassee, FL.
Abstract: Ocean vector winds measured by the SeaWinds scatterometer onboard the QuikSCAT satellite can be validated with in situ data. Ideally the comparison in situ data would be collocated in both time and space to the satellite overpass; however, this is rarely the case because of the time sampling interval of the in situ data and the sparseness of data. To compensate for the lack of ideal collocations, in situ data that are within a certain time and space range of the satellite overpass are used for comparisons. To determine the total amount of random observational error, additional uncertainty from the temporal and spatial difference must be considered along with the uncertainty associated with the data sets. The purpose of this study is to quantify the amount of error associated with the two data sets, as well as the amount of error associated with the temporal and/or spatial difference between two observations. The variance associated with a temporal difference between two observations is initially examined in an idealized case that includes only Shipboard Automated Meteorological and Oceanographic System (SAMOS) one-minute data. Temporal differences can be translated into spatial differences by using Taylor's hypothesis. The results show that as the time difference increases, the amount of variance increases. Higher wind speeds are also associated with a larger amount of variance. Collocated SeaWinds and SAMOS observations are used to determine the total variance associated with a temporal (equivalent) difference from 0 to 60 minutes. If the combined temporal and spatial difference is less than 25 minutes (equivalent), the variance associated with the temporal and spatial difference is offset by the observational errors, which are approximately 1.0 m2s-2 for wind speeds between 4 and 7 ms-1 and approximately 1.5 m2s-2 for wind speeds between 7 and 12 ms-1. If the combined temporal and spatial difference is greater than 25 minutes (equivalent), then the variance associated with the temporal and spatial difference is no longer offset by the variance associated with observational error in the data sets; therefore, the total variance gradually increases as the time difference increases.
