DiNapoli, S. (2010).
Determining the Error Characteristics of H*WIND. Master's thesis, Florida State University, Tallahassee, FL.
Abstract: The HRD Real-time Hurricane Wind Analysis System (H*Wind) is a software application used by NOAA's Hurricane Research Division to create a gridded tropical cyclone wind analysis based on a wide range of observations. One application of H*Wind fields is calibration of scatterometers for high wind speed environments. Unfortunately, the accuracy of the H*Wind product has not been studied extensively, and therefore the accuracy of scatterometer calibrations in these environments is also unknown. This investigation seeks to determine the uncertainty in the H*Wind product and estimate the contributions of several potential error sources. These error sources include random observation errors, relative bias between different data types, temporal drift resulting from combining non-simultaneous measurements, and smoothing and interpolation errors in the H*Wind software. The effects of relative bias between different data types and random observation errors are determined by performing statistical calculations on the observed wind speeds. We show that in the absence of large biases, the total contribution of all error sources results in an uncertainty of approximately 7% near the storm center, which increases to nearly 15% near the tropical storm force wind radius. The H*Wind analysis algorithm is found to introduce a positive bias to the wind speeds near the storm center, where the analyzed wind speeds are enhanced to match the highest observations. In addition, spectral analyses are performed to ensure that the filter wavelength of the final analysis product matches user specifications. With increased knowledge of these error sources and their effects, researchers will have a better understanding of the uncertainty in the H*Wind product, and can then judge the suitability of H*Wind for various research applications
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.
Holbach, H. M., Uhlhorn, E. W., & Bourassa, M. A. (2018). Off-Nadir SFMR Brightness Temperature Measurements in High-Wind Conditions.
J. Atmos. Oceanic Technol., 35(9), 1865–1879.
Abstract: Wind and wave-breaking directions are investigated as potential sources of an asymmetry identified in off-nadir remotely sensed measurements of ocean surface brightness temperatures obtained by the Stepped Frequency Microwave Radiometer (SFMR) in high-wind conditions, including in tropical cyclones. Surface wind speed, which dynamically couples the atmosphere and ocean, can be inferred from SFMR ocean surface brightness temperature measurements using a radiative transfer model and an inversion algorithm. The accuracy of the ocean surface brightness temperature to wind speed calibration relies on accurate knowledge of the surface variables that are influencing the ocean surface brightness temperature. Previous studies have identified wind direction signals in horizontally polarized radiometer measurements in low to moderate (0�20 m s−1) wind conditions over a wide range of incidence angles. This study finds that the azimuthal asymmetry in the off-nadir SFMR brightness temperature measurements is also likely a function of wind direction and extends the results of these previous studies to high-wind conditions. The off-nadir measurements from the SFMR provide critical data for improving the understanding of the relationships between brightness temperature, surface wave�breaking direction, and surface wind vectors at various incidence angles, which is extremely useful for the development of geophysical model functions for instruments like the Hurricane Imaging Radiometer (HIRAD).
Holthuijsen, L. H., Powell, M. D., & Pietrzak, J. D. (2012). Wind and waves in extreme hurricanes.
J. Geophys. Res., 117(C9), n/a-n/a.
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.
Jones, B. (2004).
Influence of Panamanian Wind Jets on the Southeast Intertropical Convergence Zone. Master's thesis, Florida State University, Tallahassee, FL.
Abstract: Gridded QuikSCAT data has been used to show that a strong confluence zone of the Southeast Pacific Intertropical Convergence Zone (SITCZ) existed in 2000 � 2002 during boreal spring, and the Panama wind jet contributes to its variability. Time series analysis of winds off the Gulf of Panama and convergence advection into the Southern Hemisphere (from 80W to 95W) show these winds kept the SE Trades out of the Northern Hemisphere and created a confluent zone in the Southern Hemisphere. A monthly averaged SITCZ is maintained by the deceleration of the SE Trades that flow from warm water toward the equatorial cold tongue, creating a speed convergent zone south of the equator. Images of wind trajectories show zonally orientated SE Trade winds that were deflected from a divergent zone parallel to the coast of South America converge with more meridional Trades over warm waters. Panamanian winds crossed into the Southern Hemisphere to contribute to this convergence. It is hypothesized that this confluent zone can be intensified by the Panamanian winds. In 2002, the SITCZ confluent zone occurred with more intense Panamanian gap flow than the previous two years. Cross equatorial SE Trades wrapped anti-cyclonically around a divergent pocket in the Northern Hemisphere and became southward winds, which allowed the Panamanian winds to enter the Southern Hemisphere and intensify the SITCZ. Variability in the Panamanian winds makes a substantial contribution to the evolution of the SITCZ.
Kara, A. B., Rochford, P. A., & Hurlburt, H. E. (2002). Air-Sea Flux Estimates And The 1997-1998 Enso Event.
Boundary-Layer Meteorology, 103(3), 439–458.