COAPS Virtual Library (Publications)

Huang, B., Hu, Z. - Z., Kinter, J. L., Wu, Z., & Kumar, A. (2012). Connection of stratospheric QBO with global atmospheric general circulation and tropical SST. Part I: methodology and composite life cycle. Clim Dyn, 38(1-2), 1–23. Keywords: Stratospheric QBO; Tropical Pacific SST; Walker circulation; Deep convection; Atmospheric stability; ERA40 and NCEP/NCAR reanalyses https://www.coaps.fsu.edu/bibliography/show.php?record=259
Zheng, Y., Zhang, R., & Bourassa, M. A. (2014). Impact of East Asian Winter and Australian Summer Monsoons on the Enhanced Surface Westerlies over the Western Tropical Pacific Ocean Preceding the El Niño Onset. J. Climate, 27(5), 1928–1944. Keywords: Atmospheric circulation; Forcing; Dynamics; Monsoons; Wind https://www.coaps.fsu.edu/bibliography/show.php?record=155
Perron, M., & Sura, P. (2013). Climatology of Non-Gaussian Atmospheric Statistics. J. Climate, 26(3), 1063–1083. Keywords: Atmospheric circulation; Extreme events; Climate variability; Climatology; Statistics; Time series https://www.coaps.fsu.edu/bibliography/show.php?record=225
Smith, S. R., Briggs, K., Lopez, N., & Kourafalou, V. (2016). Applying Automated Underway Ship Observations to Numerical Model Evaluation. J. Atmos. Oceanic Technol., 33(3), 409–428. Keywords: Ship observations; Automatic weather stations; Ocean models; Model evaluation/performance; In situ atmospheric observations; Observational techniques and algorithms; Models and modeling; In situ oceanic observations https://www.coaps.fsu.edu/bibliography/show.php?record=53
Sura, P., & Hannachi, A. (2015). Perspectives of Non-Gaussianity in Atmospheric Synoptic and Low-Frequency Variability. J. Climate, 28(13), 5091–5114. Keywords: Atmospheric circulation; Nonlinear dynamics; Statistics; Climate variability https://www.coaps.fsu.edu/bibliography/show.php?record=114
Ali, M. M., Bhat, G. S., Long, D. G., Bharadwaj, S., & Bourassa, M. A. (2013). Estimating Wind Stress at the Ocean Surface From Scatterometer Observations. IEEE Geosci. Remote Sensing Lett., 10(5), 1129–1132. Keywords: Atmospheric stability; neutral stability; scatterometer; wind stress https://www.coaps.fsu.edu/bibliography/show.php?record=184
Kent, E. C., Berry, D. I., Prytherch, J., & Roberts, J. B. (2014). A comparison of global marine surface-specific humidity datasets fromin situobservations and atmospheric reanalysis. Int. J. Climatol., 34(2), 355–376. Keywords: marine climatology; atmospheric reanalysis; surface humidity; air� sea interaction; observations; specific humidity https://www.coaps.fsu.edu/bibliography/show.php?record=164
Savage, A. C., Arbic, B. K., Alford, M. H., Ansong, J. K., Farrar, J. T., Menemenlis, D., et al. (2017). Spectral decomposition of internal gravity wave sea surface height in global models: INTERNAL GRAVITY WAVE SEA SURFACE HEIGHT. J. Geophys. Res. Oceans, 122(10), 7803–7821. Abstract: Two global ocean models ranging in horizontal resolution from 1/128 to 1/488 are used to study the space and time scales of sea surface height (SSH) signals associated with internal gravity waves (IGWs). Frequency-horizontal wavenumber SSH spectral densities are computed over seven regions of the world ocean from two simulations of the HYbrid Coordinate Ocean Model (HYCOM) and three simulations of the Massachusetts Institute of Technology general circulation model (MITgcm). High wavenumber, high-frequency SSH variance follows the predicted IGW linear dispersion curves. The realism of high-frequency motions (>0:87 cpd) in the models is tested through comparison of the frequency spectral density of dynamic height variance computed from the highest-resolution runs of each model (1/258 HYCOM and 1/488 MITgcm) with dynamic height variance frequency spectral density computed from nine in situ profiling instruments. These high-frequency motions are of particular interest because of their contributions to the small-scale SSH variability that will be observed on a global scale in the upcoming Surface Water and Ocean Topography (SWOT) satellite altimetry mission. The variance at supertidal frequencies can be comparable to the tidal and low-frequency variance for high wavenumbers (length scales smaller than 50 km), especially in the higher-resolution simulations. In the highest-resolution simulations, the high-frequency variance can be greater than the low-frequency variance at these scales. Keywords: high-frequency motions; atmospheric pressure; dynamic height https://www.coaps.fsu.edu/bibliography/show.php?record=993

Huang, B., Hu, Z. - Z., Kinter, J. L., Wu, Z., & Kumar, A. (2012). Connection of stratospheric QBO with global atmospheric general circulation and tropical SST. Part I: methodology and composite life cycle. Clim Dyn, 38(1-2), 1–23.

Zheng, Y., Zhang, R., & Bourassa, M. A. (2014). Impact of East Asian Winter and Australian Summer Monsoons on the Enhanced Surface Westerlies over the Western Tropical Pacific Ocean Preceding the El Niño Onset. J. Climate, 27(5), 1928–1944.

Perron, M., & Sura, P. (2013). Climatology of Non-Gaussian Atmospheric Statistics. J. Climate, 26(3), 1063–1083.

Smith, S. R., Briggs, K., Lopez, N., & Kourafalou, V. (2016). Applying Automated Underway Ship Observations to Numerical Model Evaluation. J. Atmos. Oceanic Technol., 33(3), 409–428.

Sura, P., & Hannachi, A. (2015). Perspectives of Non-Gaussianity in Atmospheric Synoptic and Low-Frequency Variability. J. Climate, 28(13), 5091–5114.

Ali, M. M., Bhat, G. S., Long, D. G., Bharadwaj, S., & Bourassa, M. A. (2013). Estimating Wind Stress at the Ocean Surface From Scatterometer Observations. IEEE Geosci. Remote Sensing Lett., 10(5), 1129–1132.

Kent, E. C., Berry, D. I., Prytherch, J., & Roberts, J. B. (2014). A comparison of global marine surface-specific humidity datasets fromin situobservations and atmospheric reanalysis. Int. J. Climatol., 34(2), 355–376.

Savage, A. C., Arbic, B. K., Alford, M. H., Ansong, J. K., Farrar, J. T., Menemenlis, D., et al. (2017). Spectral decomposition of internal gravity wave sea surface height in global models: INTERNAL GRAVITY WAVE SEA SURFACE HEIGHT. J. Geophys. Res. Oceans, 122(10), 7803–7821.

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