Ahern, K., Bourassa, M. A., Hart, R. E., Zhang, J. A., & Rogers, R. F. (2019). Observed Kinematic and Thermodynamic Structure in the Hurricane Boundary Layer During Intensity Change.
Mon. Wea. Rev., .
Abstract: The axisymmetric structure of the inner-core hurricane boundary layer (BL) during intensification [IN; intensity tendency ≥ 20 kt (24 h)−1], weakening [WE; intensity tendency < −10 kt (24 h)−1], and steady-state [SS; the remainder] periods are analyzed using composites of GPS dropwindsondes from reconnaissance missions between 1998 and 2015. A total of 3,091 dropsondes were composited for analysis below 2.5 km elevation—1,086 during IN, 1,042 during WE, and 963 during SS. In non-intensifying hurricanes, the lowlevel tangential wind is greater outside the radius of maximum wind (RMW) than for intensifying hurricanes, implying higher inertial stability (I) at those radii for non-intensifying hurricanes. Differences in tangential wind structure (and I) between the groups also imply differences in secondary circulation. The IN radial inflow layer is of nearly equal or greater thickness than nonintensifying groups, and all groups show an inflow maximum just outside the RMW. Non-intensifying hurricanes have stronger inflow outside the eyewall region, likely associated with frictionally forced ascent out of the BL and enhanced subsidence into the BL at radii outside the RMW. Equivalent potential temperatures (θe) and conditional stability are highest inside the RMW of non-intensifying storms, which is potentially related to TC intensity. At greater radii, inflow layer θe is lowest in WE hurricanes, suggesting greater subsidence or more convective downdrafts at those radii compared to IN and SS hurricanes. Comparisons of prior observational and theoretical studies are highlighted, especially those relating BL structure to large-scale vortex structure, convection, and intensity.
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.
Ali, M. M., Bhowmick, S. A., Sharma, R., Chaudhury, A., Pezzullo, J. C., Bourassa, M. A., et al. (2015). An Artificial Neural Network Model Function (AMF) for SARAL-Altika Winds.
IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing, 8(11), 5317–5323.
Ali, M. M., Bourassa, M. A., Bhowmick, S. A., Sharma, R., & Niharika, K. (2016). Retrieval of Wind Stress at the Ocean Surface From AltiKa Measurements.
IEEE Geosci. Remote Sensing Lett., 13(6), 821–825.
Ali, M. M., Nagamani, P. V., Sharma, N., Venu Gopal, R. T., Rajeevan, M., Goni, G. J., et al. (2015). Relationship between ocean mean temperatures and Indian summer monsoon rainfall.
Atmos. Sci. Lett., 16(3), 408–413.
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.
Armstrong, E. M., Bourassa, M. A., Cram, T. A., DeBellis, M., Elya, J., Greguska III, F. R., et al. (2019). An Integrated Data Analytics Platform.
Front. Mar. Sci., 6, 354.
Abstract: An Integrated Science Data Analytics Platform is an environment that enables the confluence of resources for scientific investigation. It harmonizes data, tools and computational resources to enable the research community to focus on the investigation rather than spending time on security, data preparation, management, etc. OceanWorks is a NASA technology integration project to establish a cloud-based Integrated Ocean Science Data Analytics Platform for big ocean science at NASA’s Physical Oceanography Distributed Active Archive Center (PO.DAAC) for big ocean science. It focuses on advancement and maturity by bringing together several NASA open-source, big data projects for parallel analytics, anomaly detection, in situ to satellite data matchup, quality-screened data subsetting, search relevancy, and data discovery. Our communities are relying on data available through distributed data centers to conduct their research. In typical investigations, scientists would (1) search for data, (2) evaluate the relevance of that data, (3) download it, and (4) then apply algorithms to identify trends, anomalies, or other attributes of the data. Such a workflow cannot scale if the research involves a massive amount of data or multi-variate measurements. With the upcoming NASA Surface Water and Ocean Topography (SWOT) mission expected to produce over 20PB of observational data during its 3-year nominal mission, the volume of data will challenge all existing Earth Science data archival, distribution and analysis paradigms. This paper discusses how OceanWorks enhances the analysis of physical ocean data where the computation is done on an elastic cloud platform next to the archive to deliver fast, web-accessible services for working with oceanographic measurements.
Bourassa, M. A. (2004). An improved sea state dependency for surface stress derived from in situ and remotely sensed winds.
Advances in Space Research, 33(7), 1136–1142.
Bourassa, M. A., and P.J. Hughes. (2018). Surface Heat Fluxes and Wind Remote Sensing. In and J. Verron J. Tintoré A. Pascual E. P. Chassignet (Ed.), (pp. 245–270). Tallahassee, FL: GODAE OceanView.
Abstract: The exchange of heat and momentum through the air-sea surface are critical aspects of ocean forcing and ocean modeling. Over most of the global oceans, there are few in situ observations that can be used to estimate these fluxes. This chapter provides background on the calculation and application of air-sea fluxes, as well as the use of remote sensing to calculate these fluxes. Wind variability makes a large contribution to variability in surface fluxes, and the remote sensing of winds is relatively mature compared to the air sea differences in temperature and humidity, which are the other key variables. Therefore, the remote sensing of wind is presented in greater detail. These details enable the reader to understand how the improper use of satellite winds can result in regional and seasonal biases in fluxes, and how to calculate fluxes in a manner that removes these biases. Examples are given of high-resolution applications of fluxes, which are used to indicate the strengths and weakness of satellite-based calculations of ocean surface fluxes.
Bourassa, M. A., Freilich, M. H., Legler, D. M., Liu, W. T., & O'Brien, J. J. (1997).
Wind observations from new satellite and research vessels agree (Vol. 78).