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.
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).
Hong, C. H., Hurlburt, H. E., Melsom, A., Metzger, E. J., Meyers, S. D., & O'Brien, J. J. (1997). The role of tropically forced coastal Kelvin waves on the stability of the coastal circulation along western north America. In G. J. Boer (Ed.), CAS/JSC Working Group, (WMO/ICSU/IOC) (Vol. 25, pp. 848–849). CAS/JSC Working Group.
Hood, M., and 39 Coauthors(including S. R. Smith). (2010). Ship-Based Repeat Hydrography: A Strategy for a Sustained Global Program. In D.ll D.E. and Stammer Harrison J. Hall (Ed.), Proceedings of OceanObs'09: Sustained Ocean Observations and Information for Society (Vol. 2).
Hoogenboom, G., C.W. Fraisse, J.W. Jones, K.T. Ingram, J.J. O'Brien, J.G. Bellow, D. Zierden, D.E. Stooksbury, J.O. Paz, A. Garcia y Garcia, L.C. Guerra, D. Letson, N.E. Breuer, V.C. Cabrera, L.U. Hatch and C. Roncoli. (2007). Climate-Based Agricultural Risk Management Tools for Florida, Georgia and Alabama, USA. In Sivakumar M.V.K., & Hansen J. (Eds.), Climate Prediction and Agriculture (pp. 273–278). Berlin, Heidelberg: Springer.
Hou, T. Y., Yan, M. P., & Wu, Z. (2009). A Variant Of The Emd Method For Multi-Scale Data. Adv. Adapt. Data Anal. , 01 (04), 483–516.
Hu, X., Cai, M., Yang, S., & Wu, Z. (2018). Delineation of thermodynamic and dynamic responses to sea surface temperature forcing associated with El Niño. Clim Dyn , 51 (11-12), 4329–4344.
Abstract: A new framework is proposed to gain a better understanding of the response of the atmosphere over the tropical Pacific to the radiative heating anomaly associated with the sea surface temperature (SST) anomaly in canonical El Niño winters. The new framework is based on the equilibrium balance between thermal radiative cooling anomalies associated with air temperature response to SST anomalies and other thermodynamic and dynamic processes. The air temperature anomalies in the lower troposphere are mainly in response to radiative heating anomalies associated with SST, atmospheric water vapor, and cloud anomalies that all exhibit similar spatial patterns. As a result, air temperature induced thermal radiative cooling anomalies would balance out most of the radiative heating anomalies in the lower troposphere. The remaining part of the radiative heating anomalies is then taken away by an enhancement (a reduction) of upward energy transport in the central-eastern (western) Pacific basin, a secondary contribution to the air temperature anomalies in the lower troposphere. Above the middle troposphere, radiative effect due to water vapor feedback is weak. Thermal radiative cooling anomalies are mainly in balance with the sum of latent heating anomalies, vertical and horizontal energy transport anomalies associated with atmospheric dynamic response and the radiative heating anomalies due to changes in cloud. The pattern of Gill-type response is attributed mainly to the non-radiative heating anomalies associated with convective and large-scale energy transport. The radiative heating anomalies associated with the anomalies of high clouds also contribute positively to the Gill-type response. This sheds some light on why the Gill-type atmospheric response can be easily identifiable in the upper atmosphere.
Huang, N. E., Wu, Z., Long, S. R., Arnold, K. C., Chen, X., & Blank, K. (2009). On Instantaneous Frequency. Adv. Adapt. Data Anal. , 01 (02), 177–229.
Huang, N. E., Wu, Z., Pinzón, J. E., Parkinson, C. L., Long, S. R., Blank, K., et al. (2009). Reductions Of Noise And Uncertainty In Annual Global Surface Temperature Anomaly Data. Adv. Adapt. Data Anal. , 01 (03), 447–460.
