|Home||<< 1 >>|
|Arguez, A., Bourassa, M. A., & O'Brien, J. J. (2005). Detection of the MJO Signal from QuikSCAT. J. Atmos. Oceanic Technol., 22(12), 1885–1894.|
|Arguez, A., Yu, P., & O'Brien, J. J. (2008). A New Method for Time Series Filtering near Endpoints. J. Atmos. Oceanic Technol., 25(4), 534–546.|
|Bourassa, M. A., & McBeth Ford, K. (2010). Uncertainty in Scatterometer-Derived Vorticity. J. Atmos. Oceanic Technol., 27(3), 594–603.|
|DiNapoli, S. M., Bourassa, M. A., & Powell, M. D. (2012). Uncertainty and Intercalibration Analysis of H*Wind. J. Atmos. Oceanic Technol., 29(6), 822–833.|
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).
Keywords: Tropical cyclones; Wind; Air-sea interaction; Microwave observations; Remote sensing; Surface observations
|Hughes, P. J., Bourassa, M. A., Rolph, J. J., & Smith, S. R. (2012). Averaging-Related Biases in Monthly Latent Heat Fluxes. J. Atmos. Oceanic Technol., 29(7), 974–986.|
|Morey, S. L., & Dukhovskoy, D. S. (2012). Analysis Methods for Characterizing Salinity Variability from Multivariate Time Series Applied to the Apalachicola Bay Estuary. J. Atmos. Oceanic Technol., 29(4), 613–628.|
|Smith, S. R., Bourassa, M. A., & Sharp, R. J. (1999). Establishing More Truth in True Winds. J. Atmos. Oceanic Technol., 16(7), 939–952.|
|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.|
|Weissman, D. E., Bourassa, M. A., & Tongue, J. (2002). Effects of Rain Rate and Wind Magnitude on SeaWinds Scatterometer Wind Speed Errors. J. Atmos. Oceanic Technol., 19(5), 738–746.|