Maue, R. N., & Hart, R. E. (2007). Comment on “Low frequency variability in globally integrated tropical cyclone power dissipation” by Ryan Sriver and Matthew Huber. Geophys. Res. Lett. , 34 (11).
May, J. (2010). Quantifying Variance Due to Temporal and Spatial Difference Between Ship and Satellite Winds . Master's thesis, Florida State University, Tallahassee, FL.
Abstract: Ocean vector winds measured by the SeaWinds scatterometer onboard the QuikSCAT satellite can be validated with in situ data. Ideally the comparison in situ data would be collocated in both time and space to the satellite overpass; however, this is rarely the case because of the time sampling interval of the in situ data and the sparseness of data. To compensate for the lack of ideal collocations, in situ data that are within a certain time and space range of the satellite overpass are used for comparisons. To determine the total amount of random observational error, additional uncertainty from the temporal and spatial difference must be considered along with the uncertainty associated with the data sets. The purpose of this study is to quantify the amount of error associated with the two data sets, as well as the amount of error associated with the temporal and/or spatial difference between two observations. The variance associated with a temporal difference between two observations is initially examined in an idealized case that includes only Shipboard Automated Meteorological and Oceanographic System (SAMOS) one-minute data. Temporal differences can be translated into spatial differences by using Taylor's hypothesis. The results show that as the time difference increases, the amount of variance increases. Higher wind speeds are also associated with a larger amount of variance. Collocated SeaWinds and SAMOS observations are used to determine the total variance associated with a temporal (equivalent) difference from 0 to 60 minutes. If the combined temporal and spatial difference is less than 25 minutes (equivalent), the variance associated with the temporal and spatial difference is offset by the observational errors, which are approximately 1.0 m2s-2 for wind speeds between 4 and 7 ms-1 and approximately 1.5 m2s-2 for wind speeds between 7 and 12 ms-1. If the combined temporal and spatial difference is greater than 25 minutes (equivalent), then the variance associated with the temporal and spatial difference is no longer offset by the variance associated with observational error in the data sets; therefore, the total variance gradually increases as the time difference increases.
McDonald, E. M. (2007). Designing Reliable Large-Scale Storage Arrays . Master's thesis, Florida State University, Tallahassee, FL.
Abstract: Large-scale storage arrays are always in high demand by universities, government agencies, web search engines, and research laboratories. This unvarying need for more data storage has begun to push storage array magnitudes into an unknown stratum. As storage systems continue to outgrow the terabyte class and move into the petabyte range, these colossal arrays begin to show design limitations. This thesis focuses primarily on disk drives as the building blocks of reliable large-scale storage arrays. As a feasibility baseline, the overall reliability of large-scale storage arrays should be greater than that of a single disk. However, petabyte- and exabyte-sized systems, requiring thousands to millions of disk drives, present a serious challenge in terms of reliability. Therefore, multi-level redundancy schemes must be used in order to slow these dwindling reliabilities. This work, based upon the previous research of redundant arrays of independent disks (RAID) by Patterson et al., introduces the reliability analysis of dual- and tri-level Grouped RAID (GRAID) configurations. As storage arrays rapidly increase in size, the use of multi-level redundancy is essential. Design recommendations for various large-scale storage arrays, ranging from 100 Tebibytes (TiB) to 100 Exbibytes (EiB), can be generated using the custom reliability calculator tool written in MATLAB. The analysis of these design recommendations shows that dual-level GRAID configurations are only recommended for array magnitudes up to 5 PiB. Beyond this threshold, tri-level GRAID demonstrates feasibility for storage magnitudes up to 100 EiB and beyond.
Melsom, A., Meyers, S. D., O'Brien, J. J., Hurlburt, H. E., & Metzger, J. E. (1999). ENSO Effects on Gulf of Alaska Eddies. Earth Interact. , 3 (1), 1–30.
Mende, M., & Misra, V. (2020). Time to Flatten the Curves on COVID-19 and Climate Change. Marketing Can Help. Journal of Public Policy & Marketing , .
Abstract: The health, economic, and social impact of the COVID-19 pandemic is unprecedented in our lifetime, and no individual in this globalized, interconnected world is immune from its effects. This pandemic is a fundamental challenge for consumers, companies, and governments. Against this background, our commentary underscores linkages between public health, environment, and economy and explores how lessons from COVID-19 can help prevent other large-scale disasters.1 We focus on global climate change (GCC), because rising temperatures increase the likelihood of future pandemics.2 Accordingly, experts consider GCC “the largest public health threat of the century” (Wyns 2020). Although societal crises are underresearched in marketing, we propose that marketers should add their expertise to help avoid future crises. Notably, the Journal of Public Policy & Marketing (JPP&M) is uniquely positioned as a premier outlet for corresponding research at the intersection of marketing and policy.
Metzger, E. J., H.E. Hurlburt, A.J. Wallcraft, O.M. Smedstad, J.A. Cummings, and E.P. Chassignet. (2009). Predicting Ocean Weather using the HYbrid Coordinate Ocean Model (HYCOM). NRL Review , , submitted.
Meyers, S. D., & O'Brien, J. J. (1997). Tropically forced Rossby waves as a source of decadal variability in the Northeast Pacific Ocean. In Seventh AMS Conference on Climate Variations, Long Beach, CA, USA (pp. 92–96).
Meyers, S. D., & Basu, S. (1999). Eddies in the eastern Gulf of Alaska from TOPEX/POSEIDON altimetry. J. Geophys. Res. , 104 (C6), 13333–13343.
Meyers, S. D., & O'Brien, J. J. (1995). Pacific Ocean influences atmospheric carbon dioxide. Eos Trans. AGU , 76 (52), 533.
Meyers, S. D., & O'Brien, J. J. (1998). Historical reconstruction of the 1868-1993 ENSO cycle using EOF climate basis functions. In 9th Conference on Interaction of the Sea and Atmosphere at the 78th American-Meteorogical-Society Annual Meeting (pp. 203–233).
