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).
Bourassa, M. A., Legler, D. M., O'Brien, J. J., & Smith, S. R. (2003). SeaWinds validation with research vessels.
J. Geophys. Res., 108(C2).
Bourassa, M. A., and P.J. Hughes. (2018). Surface Heat Fluxes and Wind Remote Sensing. In and J. Verron J. Tintoré A. Pascual E. P. Chassignet (Ed.), (pp. 245–270). Tallahassee, FL: GODAE OceanView.
Abstract: The exchange of heat and momentum through the air-sea surface are critical aspects of ocean forcing and ocean modeling. Over most of the global oceans, there are few in situ observations that can be used to estimate these fluxes. This chapter provides background on the calculation and application of air-sea fluxes, as well as the use of remote sensing to calculate these fluxes. Wind variability makes a large contribution to variability in surface fluxes, and the remote sensing of winds is relatively mature compared to the air sea differences in temperature and humidity, which are the other key variables. Therefore, the remote sensing of wind is presented in greater detail. These details enable the reader to understand how the improper use of satellite winds can result in regional and seasonal biases in fluxes, and how to calculate fluxes in a manner that removes these biases. Examples are given of high-resolution applications of fluxes, which are used to indicate the strengths and weakness of satellite-based calculations of ocean surface fluxes.
Dukhovskoy, D., & Bourassa, M. (2011). Comparison of ocean surface wind products in the perspective of ocean modeling of the Nordic Seas. In
Hilburn, K. A. (2003). Development of scatterometer-derived surface pressures for the Southern Ocean.
J. Geophys. Res., 108(C7).
Hite, M. M. (2006).
Vorticity-Based Detection of Tropical Cyclogenesis. Master's thesis, Florida State University, Tallahassee, FL.
Abstract: Ocean wind vectors from the SeaWinds scatterometer on QuikSCAT and GOES imagery are used to develop an objective technique that can detect and monitor tropical disturbances associated with the early stages of tropical cyclogenesis in the Atlantic basin. The technique is based on identification of surface vorticity and wind speed signatures that exceed certain threshold magnitudes, with vorticity averaged over an appropriate spatial scale. The threshold values applied herein are determined from the precursors of 15 tropical cyclones during the 1999-2004 Atlantic hurricane seasons using research-quality QuikSCAT data. Tropical disturbances are found for these cases within a range of 19 hours to 101 hours before classification as tropical cyclones by the National Hurricane Center (NHC). The 15 cases are further subdivided based upon their origination source (i.e., easterly wave, upper-level cut-off low, stagnant frontal zone, etc). Primary focus centers on the cases associated with tropical waves, since these waves account for approximately 63% of all Atlantic tropical cyclones. The detection technique illustrates the ability to track these tropical disturbances from near the coast of Africa. Analysis of the pre-tropical cyclone (TC) tracks for these cases depict stages, related to wind speed and precipitation, in the evolution of an easterly wave to tropical cyclone.
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.
Hughes, P. J. (2014).
The Influence of Small-Scale Sea Surface Temperature Gradients on Surface Vector Winds and Subsequent Impacts on Oceanic Ekman Pumping. Tallahassee, FL: Florida State University.
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.