scholarly journals Modeled and observed empirical orthogonal functions of currents in the Yucatan Channel, Gulf of Mexico

2004 ◽  
Vol 109 (C8) ◽  
pp. n/a-n/a ◽  
Author(s):  
Lie-Yauw Oey ◽  
Tal Ezer ◽  
Wilton Sturges
Keyword(s):  
Gulf Of Mexico ◽  
Empirical Orthogonal Functions ◽  
Orthogonal Functions ◽  
Yucatan Channel

scholarly journals Predicting Atlantic Tropical Cyclone Seasonal Activity in April

2009 ◽  
Vol 24 (2) ◽  
pp. 436-455 ◽  
Author(s):  
Elinor Keith ◽  
Lian Xie
Keyword(s):  
Tropical Cyclone ◽  
Gulf Of Mexico ◽  
Confidence Level ◽  
Complex Problem ◽  
Empirical Orthogonal Functions ◽  
Orthogonal Functions ◽  
Random Chance ◽  
Major Hurricane ◽  
Hurricane Activity ◽  
The U.S

Abstract Seasonal hurricane forecasts are continuing to develop skill, although they are still subject to large uncertainties. This study uses a new methodology of cross-correlating variables against empirical orthogonal functions (EOFs) of the hurricane track density function (HTDF) to select predictors. These predictors are used in a regression model for forecasting seasonal named storm, hurricane, and major hurricane activity in the entire Atlantic, the Caribbean Sea, and the Gulf of Mexico. In addition, a scheme for predicting landfalling tropical systems along the U.S. Gulf of Mexico, southeastern, and northeastern coastlines is developed, but predicting landfalling storms adds an extra layer of uncertainty to an already complex problem, and on the whole these predictions do not perform as well. The model performs well in the basin-wide predictions over the entire Atlantic and Caribbean, with the predictions showing an improvement over climatology and random chance at a 95% confidence level. Over the Gulf of Mexico, only named storms showed that level of predictability. Predicting landfalls proves more difficult, and only the prediction of named storms along the U.S. southeastern and Gulf coasts shows an improvement over random chance at the 95% confidence level. Tropical cyclone activity along the U.S. northeastern coast is found to be unpredictable in this model; with the rarity of events, the model is unstable.

Empirical Methods in Short-Term Climate Prediction

2006 ◽  
Author(s):  
Huug van den Dool
Keyword(s):  
Climate Prediction ◽  
Empirical Orthogonal Functions ◽  
Data Sets ◽  
Seasonal Forecasts ◽  
Short Term ◽  
Orthogonal Functions ◽  
Ocean Atmosphere Interaction ◽  
Global Coverage ◽  
Atmosphere Interaction ◽  
Global Data

This clear and accessible text describes the methods underlying short-term climate prediction at time scales of 2 weeks to a year. Although a difficult range to forecast accurately, there have been several important advances in the last ten years, most notably in understanding ocean-atmosphere interaction (El Nino for example), the release of global coverage data sets, and in prediction methods themselves. With an emphasis on the empirical approach, the text covers in detail empirical wave propagation, teleconnections, empirical orthogonal functions, and constructed analogue. It also provides a detailed description of nearly all methods used operationally in long-lead seasonal forecasts, with new examples and illustrations. The challenges of making a real time forecast are discussed, including protocol, format, and perceptions about users. Based where possible on global data sets, illustrations are not limited to the Northern Hemisphere, but include several examples from the Southern Hemisphere.

scholarly journals Spatio-Temporal Patterns of Mass Changes in Himalayan Glaciated Region from EOF Analyses of GRACE Data

2021 ◽  
Vol 13 (2) ◽  
pp. 265
Author(s):  
Harika Munagapati ◽  
Virendra M. Tiwari
Keyword(s):  
Temperature Anomaly ◽  
Mass Gain ◽  
Snow Accumulation ◽  
Empirical Orthogonal Functions ◽  
Total Water ◽  
Orthogonal Functions ◽  
Grace Data ◽  
Grace Satellite ◽  
Spatio Temporal ◽  
Gravity Recovery

The nature of hydrological seasonality over the Himalayan Glaciated Region (HGR) is complex due to varied precipitation patterns. The present study attempts to exemplify the spatio-temporal variation of hydrological mass over the HGR using time-variable gravity from the Gravity Recovery and Climate Experiment (GRACE) satellite for the period of 2002–2016 on seasonal and interannual timescales. The mass signal derived from GRACE data is decomposed using empirical orthogonal functions (EOFs), allowing us to identify the three broad divisions of HGR, i.e., western, central, and eastern, based on the seasonal mass gain or loss that corresponds to prevailing climatic changes. Further, causative relationships between climatic variables and the EOF decomposed signals are explored using the Granger causality algorithm. It appears that a causal relationship exists between total precipitation and total water storage from GRACE. EOF modes also indicate certain regional anomalies such as the Karakoram mass gain, which represents ongoing snow accumulation. Our causality result suggests that the excessive snowfall in 2005–2008 has initiated this mass gain. However, as our results indicate, despite the dampening of snowfall rates after 2008, mass has been steadily increasing in the Karakorum, which is attributed to the flattening of the temperature anomaly curve and subsequent lower melting after 2008.

scholarly journals The Flow through the Gulf of Mexico

2019 ◽  
Vol 49 (6) ◽  
pp. 1381-1401 ◽  
Author(s):  
J. Candela ◽  
J. Ochoa ◽  
J. Sheinbaum ◽  
M. López ◽  
P. Pérez-Brunius ◽  
...  
Keyword(s):  
Gulf Of Mexico ◽  
Straits Of Florida ◽  
The North ◽  
Astronomical Tide ◽  
Flow Through ◽  
Sverdrup Transport ◽  
The Mean ◽  
Yucatan Channel ◽  
Range Variation

AbstractFour years (September 2012 to August 2016) of simultaneous current observations across the Yucatan Channel (~21.5°N) and the Straits of Florida (~81°W) have permitted us to investigate the characteristics of the flow through the Gulf of Mexico. The average transport in both channels is 27.6 Sv (1 Sv = 106 m3 s−1), in accordance with previous estimates. At the Straits of Florida section, the transport related to the astronomical tide explains 55% of the observed variance with a mixed semidiurnal/diurnal character, while in the Yucatan Channel tides contribute 82% of the total variance and present a dominant diurnal character. At periods longer than a week the transports in the Yucatan and Florida sections have a correlation of 0.83 without any appreciable lag. The yearly running means of the transport time series in both channels are well correlated (0.98) and present a 3-Sv range variation in the 4 years analyzed. This long-term variability is well related to the convergence of the Sverdrup transport in the North Atlantic between 14.25° and 18.75°N. Using 2 years (July 2014–July 2016) of simultaneous currents observations in the Florida section, the Florida Cable section (~26.7°N), and a section across the Old Bahama Channel (~78.4°W), a mean northward transport of 28.4, 31.1, and 1.6 Sv, respectively, is obtained, implying that only 1.1 Sv is contributed by the Northwest Providence Channel to the mean transport observed at the Cable section during this 2-yr period.

Identifying VIV Vibration Modes by Use of the Empirical Orthogonal Functions Technique

2002 ◽  
Author(s):  
Gudmund Kleiven
Keyword(s):  
Model Test ◽  
Vibration Mode ◽  
Multivariate Data ◽  
Empirical Orthogonal Functions ◽  
Mode Shapes ◽  
Data Sets ◽  
Vibration Modes ◽  
Ocean Engineering ◽  
Orthogonal Functions ◽  
Related Technique

The Empirical Orthogonal Functions (EOF) technique has widely being used by oceanographers and meteorologists, while the Singular Value Decomposition (SVD being a related technique is frequently used in the statistics community. Another related technique called Principal Component Analysis (PCA) is observed being used for instance in pattern recognition. The predominant applications of these techniques are data compression of multivariate data sets which also facilitates subsequent statistical analysis of such data sets. Within Ocean Engineering the EOF technique is not yet widely in use, although there are several areas where multivariate data sets occur and where the EOF technique could represent a supplementary analysis technique. Examples are oceanographic data, in particular current data. Furthermore data sets of model- or full-scale data of loads and responses of slender bodies, such as pipelines and risers are relevant examples. One attractive property of the EOF technique is that it does not require any a priori information on the physical system by which the data is generated. In the present paper a description of the EOF technique is given. Thereafter an example on use of the EOF technique is presented. The example is analysis of response data from a model test of a pipeline in a long free span exposed to current. The model test program was carried out in order to identify the occurrence of multi-mode vibrations and vibration mode amplitudes. In the present example the EOF technique demonstrates the capability of identifying predominant vibration modes of inline as well as cross-flow vibrations. Vibration mode shapes together with mode amplitudes and frequencies are also estimated. Although the present example is not sufficient for concluding on the applicability of the EOF technique on a general basis, the results of the present example demonstrate some of the potential of the technique.

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