Strazzo, S. E., Elsner, J. B., LaRow, T. E., Murakami, H., Wehner, M., & Zhao, M. (2016). The influence of model resolution on the simulated sensitivity of North Atlantic tropical cyclone maximum intensity to sea surface temperature. J. Adv. Model. Earth Syst. , 8 (3), 1037–1054.
Nagamani, P. V., Ali, M. M., Goni, G. J., Udaya Bhaskar, T. V. S., McCreary, J. P., Weller, R. A., et al. (2016). Heat content of the Arabian Sea Mini Warm Pool is increasing. Atmos. Sci. Lett. , 17 (1), 39–42.
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).
Hart, R. E., Maue, R. N., & Watson, M. C. (2007). Estimating Local Memory of Tropical Cyclones through MPI Anomaly Evolution. Mon. Wea. Rev. , 135 (12), 3990–4005.
Holbach, H. M., & Bourassa, M. A. (2014). The Effects of Gap-Wind-Induced Vorticity, the Monsoon Trough, and the ITCZ on East Pacific Tropical Cyclogenesis. Mon. Wea. Rev. , 142 (3), 1312–1325.
Subrahmanyam, B., Murty, V. S. N., Sharp, R. J., & O'Brien, J. J. (2005). Air-sea Coupling During the Tropical Cyclones in the Indian Ocean: A Case Study Using Satellite Observations. Pure appl. geophys. , 162 (8-9), 1643–1672.
Peng, M. S., Maue, R. N., Reynolds, C. A., & Langland, R. H. (2007). Hurricanes Ivan, Jeanne, Karl (2004) and mid-latitude trough interactions. Meteorol. Atmos. Phys. , 97 (1-4), 221–237.
LaRow, T. (2013). An analysis of tropical cyclones impacting the Southeast United States from a regional reanalysis. Reg Environ Change , 13 (S1), 35–43.
Morey, S. L., Bourassa, M. A., Dukhovskoy, D. S., & O'Brien, J. J. (2006). Modeling studies of the upper ocean response to a tropical cyclone. Ocean Dynamics , 56 (5-6), 594–606.
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