Cammarano, D., Stefanova, L., Ortiz, B. V., Ramirez-Rodrigues, M., Asseng, S., Misra, V., et al. (2013). Evaluating the fidelity of downscaled climate data on simulated wheat and maize production in the southeastern US.
Reg Environ Change, 13(S1), 101–110.
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
DiNapoli, S. M., & Misra, V. (2012). Reconstructing the 20th century high-resolution climate of the southeastern United States.
J. Geophys. Res., 117(D19), n/a-n/a.
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.
Misra, V., & DiNapoli, S. (2014). The variability of the Southeast Asian summer monsoon.
Int. J. Climatol., 34(3), 893–901.
Misra, V., & DiNapoli, S. M. (2013). The observed teleconnection between the equatorial Amazon and the Intra-Americas Seas.
Clim Dyn, 40(11-12), 2637–2649.
Misra, V., & DiNapoli, S. M. (2013). Understanding the wet season variations over Florida.
Clim Dyn, 40(5-6), 1361–1372.
Misra, V., DiNapoli, S., & Powell, M. (2013). The Track Integrated Kinetic Energy of Atlantic Tropical Cyclones.
Mon. Wea. Rev., 141(7), 2383–2389.
Misra, V., DiNapoli, S. M., & Bastola, S. (2013). Dynamic downscaling of the twentieth-century reanalysis over the southeastern United States.
Reg Environ Change, 13(S1), 15–23.
Misra, V., Li, H., Wu, Z., & DiNapoli, S. (2014). Global seasonal climate predictability in a two tiered forecast system: part I: boreal summer and fall seasons.
Clim Dyn, 42(5-6), 1425–1448.