Davis, S. R., Bourassa, M. A., Atlas, R., Ardizzone, J., Brin, E., O'Brien, J. J., et al. (2003).
Near-realtime sea surface pressure Fields from NASA's SeaWinds scatterometer and their impact in NWP (H. Ritchie, Ed.). CAS/JSC Working Group on Numerical Experimentation, Research Activities in Atmospheric and Oceanic Modeling. Geneva, Switzerland: World Meteorological Organization.
Davis, X. J. (2002).
Evaluation of wind products for forcing coastal ocean models. Master's thesis, Florida State University, Tallahassee, FL.
De Souza-Machado, S., Tangborn, A., Sura, P., Hepplewhite, C., & Strow, L. L. (2017). Non-Gaussian Analysis of Observations from the Atmospheric Infrared Sounder Compared with ERA and MERRA Reanalyses.
J. Appl. Meteor. Climatol., 56(5), 1463–1481.
Decima, M., Landry, M. R., Stukel, M. R., Lopez-Lopez, L., & Krause, J. W. (2016). Mesozooplankton biomass and grazing in the Costa Rica Dome: amplifying variability through the plankton food web.
J Plankton Res, 38(2), 317–330.
Abstract: We investigated standing stocks and grazing rates of mesozooplankton assemblages in the Costa Rica Dome (CRD), an open-ocean upwelling ecosystem in the eastern tropical Pacific. While phytoplankton biomass in the CRD is dominated by picophytoplankton (<2-microm cells) with especially high concentrations of Synechococcus spp., we found high mesozooplankton biomass ( approximately 5 g dry weight m-2) and grazing impact (12-50% integrated water column chlorophyll a), indicative of efficient food web transfer from primary producers to higher levels. In contrast to the relative uniformity in water-column chlorophyll a and mesozooplankton biomass, variability in herbivory was substantial, with lower rates in the central dome region and higher rates in areas offset from the dome center. While grazing rates were unrelated to total phytoplankton, correlations with cyanobacteria (negative) and biogenic SiO2 production (positive) suggest that partitioning of primary production among phytoplankton sizes contributes to the variability observed in mesozooplankton metrics. We propose that advection of upwelled waters away from the dome center is accompanied by changes in mesozooplankton composition and grazing rates, reflecting small changes within the primary producers. Small changes within the phytoplankton community resulting in large changes in the mesozooplankton suggest that the variability in lower trophic level dynamics was effectively amplified through the food web.
Deng, J., Wu, Z., Zhang, M., Huang, N. E., Wang, S., & Qiao, F. (2018). Using Holo-Hilbert spectral analysis to quantify the modulation of Dansgaard-Oeschger events by obliquity.
Quaternary Science Reviews, 192, 282–299.
Abstract: Astronomical forcing (obliquity and precession) has been thought to modulate Dansgaard-Oeschger (DO) events, yet the detailed quantification of such modulations has not been examined. In this study, we apply the novel Holo-Hilbert Spectral Analysis (HHSA) to five polar ice core records, quantifying astronomical forcing's time-varying amplitude modulation of DO events and identifying the preferred obliquity phases for large amplitude modulations. The unique advantages of HHSA over the widely used windowed Fourier spectral analysis for quantifying astronomical forcing's nonlinear modulations of DO events is first demonstrated with a synthetic data that closely resembles DO events recorded in Greenland ice cores (NGRIP, GRIP, and GISP2 cores on GICC05 modelext timescale). The analysis of paleoclimatic proxies show that statistically significantly more frequent DO events, with larger amplitude modulation in the Greenland region, tend to occur in the decreasing phase of obliquity, especially from its mean value to its minimum value. In the eastern Antarctic, although statistically significantly more DO events tend to occur in the decreasing obliquity phase in general, the preferred phase of obliquity for large amplitude modulation on DO events is a segment of the increasing phase near the maximum obliquity, implying that the physical mechanisms of DO events may be different for the two polar regions. Additionally, by using cross-spectrum and magnitude-squared analyses, Greenland DO mode at a timescale of about 1400 years leads the Antarctic DO mode at the same timescale by about 1000 years. (C) 2018 Elsevier Ltd. All rights reserved.
Deng, J., Wu, Z., Zhang, M., Huang, N. E., Wang, S., & Qiao, F. (2019). Data concerning statistical relation between obliquity and Dansgaard-Oeschger events.
Data Brief, 23.
Abstract: Data presented are related to the research article entitled “Using Holo-Hilbert spectral analysis to quantify the modulation of Dansgaard-Oeschger events by obliquity” (J. Deng et al., 2018). The datasets in Deng et al. (2018) are analyzed on the foundation of ensemble empirical mode decomposition (EEMD) (Z.H. Wu and N.E. Huang, 2009), and reveal more occurrences of Dansgaard-Oeschger (DO) events in the decreasing phase of obliquity. Here, we report the number of significant high Shannon entropy (SE) (C.E. Shannon and W. Weaver, 1949) of 95% significance level of DO events in the increasing and decreasing phases of obliquity, respectively. First, the proxy time series are filtered by EEMD to obtain DO events. Then, the time-varying SE of DO modes are calculated on the basis of principle of histogram. The 95% significance level is evaluated through surrogate data (T. Schreiber and A. Schmitz, 1996). Finally, a comparison between the numbers of SE values that are larger than 95% significance level in the increasing and decreasing phases of obliquity, respectively, is reported.
Deremble, B., Dewar, W. K., & Chassignet, E. P. (2016). Vorticity dynamics near sharp topographic features.
J Mar Res, 74(6), 249–276.
Devanas, A., & Stefanova, L. (2018). Statistical Prediction Of Waterspout Probability For The Florida Keys.
Wea. Forecasting, 33, 389–410.
Abstract: A statistical model of waterspout probability was developed for wet-season (June–September) days over the Florida Keys. An analysis was performed on over 200 separate variables derived from Key West 1200 UTC daily wet-season soundings during the period 2006–14. These variables were separated into two subsets: days on which a waterspout was reported anywhere in the Florida Keys coastal waters and days on which no waterspouts were reported. Days on which waterspouts were reported were determined from the National Weather Service (NWS) Key West local storm reports. The sounding at Key West was used for this analysis since it was assumed to be representative of the atmospheric environment over the area evaluated in this study. The probability of a waterspout report day was modeled using multiple logistic regression with selected predictors obtained from the sounding variables. The final model containing eight separate variables was validated using repeated fivefold cross validation, and its performance was compared to that of an existing waterspout index used as a benchmark. The performance of the model was further validated in forecast mode using an independent verification wet-season dataset from 2015–16 that was not used to define or train the model. The eight-predictor model was found to produce a probability forecast with robust skill relative to climatology and superior to the benchmark waterspout index in both the cross validation and in the independent verification.
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.