Henson, J. I., Muller-Karger, F., Wilson, D., Morey, S. L., Maul, G. A., Luther, M., et al. (2006). Strategic geographic positioning of sea level gauges to aid in early detection of tsunamis in the Intra-Americas Sea. Science of Tsunami Hazards , 25 (3), 173–207.
Hernandez, F., Bertino, L., Brassington, G., Chassignet, E., Cummings, james, Davidson, F., et al. (2009). Validation and Intercomparison Studies Within GODAE. Oceanog. , 22 (3), 128–143.
Hilburn, K. (2002). Development of scatterometer-derived research quality surface pressure fields for the Southern Ocean . Master's thesis, Florida State University, Tallahassee, FL.
Hilburn, K. A. (2003). Development of scatterometer-derived surface pressures for the Southern Ocean. J. Geophys. Res. , 108 (C7).
Hilburn, K. A., Bourassa, M. A., & O'Brien, J. J. (2002). Development of scatterometer-derived research-quality surface pressure fields for the Southern Ocean . Orlando, FL: AMS.
Hite, M., Bourassa, M. A., & O'Brien, J. J. (2006). Objective detection of Atlantic tropical disturbances. In 14th Conference on Interactions of the Sea and Atmosphere, AMS, Monterey, CA, USA (cdrom).
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.
Hite, M. M., Bourassa, M. A., & O'Brien, J. J. (2006). Vorticity-Based Detection Of Tropical Cyclones. In 14th Conference on Interactions of the Sea and Atmosphere, American Meteorological Society, Atlanta, Ga, USA (cdrom).
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.
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.
