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Chen, R., Ebrahimi, H., & Jones, W. L. (2017). Creating a Multidecadal Ocean Microwave Brightness Dataset: Three-Way Intersatellite Radiometric Calibration Among GMI, TMI, and WindSat. IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing, 10(6), 2623–2630.
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Katsaros, K. B., A. Bentamy, M. Bourassa, N. Ebuchi, J. Gower, W. T. Liu, and S. Vignudelli. (2011). Climate Data Issues from an Oceanographic Remote Sensing Perspective. In Remote Sensing of the Changing Oceans (pp. 7–32). Berlin, Germany: Springer-Verlag.
Abstract: In this chapter we review several climatologically important variables
with a history of observation from spaceborne platforms. These include sea surface
temperature and wind vectors, altimetric estimates of sea surface height, energy and
water vapor fluxes at the sea surface, precipitation over the ocean, and ocean color.
We then discuss possible improvements in sampling for climate and climate change
definition. Issues of consistency of different data sources, archiving and distribution
of these types of data are discussed. The practical prospect of immediate international
coordination through the concept of virtual constellations is discussed and
applauded.
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Ma, J., Foltz, G. R., Soden, B. J., Huang, G., He, J., & Dong, C. (2016). Will surface winds weaken in response to global warming? Environ. Res. Lett., 11(12), 124012.
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Trepanier, J. C., Needham, H. F., Elsner, J. B., & Jagger, T. H. (2015). Combining Surge and Wind Risk from Hurricanes Using a Copula Model: An Example from Galveston, Texas. The Professional Geographer, 67(1), 52–61.
Abstract: Consideration of climate-related impacts on coasts is important to ensure readiness for disaster response. Local risk of storm surge and strong winds from hurricanes affecting Galveston, Texas, is quantified using a bivariate copula model fit to observed data. The model uses a two-dimensional Archimedean copula. Parametric uncertainty (5th and 95th percentiles) is quantified using a Monte Carlo procedure. The annual probability of a hurricane producing winds of at least 50 ms−1 and a surge of at least 4 m is 1.7 percent with a 95 percent confidence interval of (1.33, 1.78) percent. The methodology can be extended to include inland flooding and can be applied elsewhere with available information.
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