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.
Wentz, F. J., Ricciardulli, L., Rodriguez, E., Stiles, B. W., Bourassa, M. A., Long, D. G., et al. (2017). Evaluating and Extending the Ocean Wind Climate Data Record.
IEEE J Sel Top Appl Earth Obs Remote Sens, 10(5), 2165–2185.
Abstract: Satellite microwave sensors, both active scatterometers and passive radiometers, have been systematically measuring near-surface ocean winds for nearly 40 years, establishing an important legacy in studying and monitoring weather and climate variability. As an aid to such activities, the various wind datasets are being intercalibrated and merged into consistent climate data records (CDRs). The ocean wind CDRs (OW-CDRs) are evaluated by comparisons with ocean buoys and intercomparisons among the different satellite sensors and among the different data providers. Extending the OW-CDR into the future requires exploiting all available datasets, such as OSCAT-2 scheduled to launch in July 2016. Three planned methods of calibrating the OSCAT-2 sigmao measurements include 1) direct Ku-band sigmao intercalibration to QuikSCAT and RapidScat; 2) multisensor wind speed intercalibration; and 3) calibration to stable rainforest targets. Unfortunately, RapidScat failed in August 2016 and cannot be used to directly calibrate OSCAT-2. A particular future continuity concern is the absence of scheduled new or continuation radiometer missions capable of measuring wind speed. Specialized model assimilations provide 30-year long high temporal/spatial resolution wind vector grids that composite the satellite wind information from OW-CDRs of multiple satellites viewing the Earth at different local times.
Nguyen, T. T. (2014).
Variability of Cross-Slope Flow in the Desoto Canyon Region. Master's thesis, Florida State University, Tallahassee, FL.
Hughes, P. J. (2014).
The Influence of Small-Scale Sea Surface Temperature Gradients on Surface Vector Winds and Subsequent Impacts on Oceanic Ekman Pumping. Tallahassee, FL: Florida State University.
Hite, M. M. (2006).
Vorticity-Based Detection of Tropical Cyclogenesis. Master's thesis, Florida State University, Tallahassee, FL.
Abstract: Ocean wind vectors from the SeaWinds scatterometer on QuikSCAT and GOES imagery are used to develop an objective technique that can detect and monitor tropical disturbances associated with the early stages of tropical cyclogenesis in the Atlantic basin. The technique is based on identification of surface vorticity and wind speed signatures that exceed certain threshold magnitudes, with vorticity averaged over an appropriate spatial scale. The threshold values applied herein are determined from the precursors of 15 tropical cyclones during the 1999-2004 Atlantic hurricane seasons using research-quality QuikSCAT data. Tropical disturbances are found for these cases within a range of 19 hours to 101 hours before classification as tropical cyclones by the National Hurricane Center (NHC). The 15 cases are further subdivided based upon their origination source (i.e., easterly wave, upper-level cut-off low, stagnant frontal zone, etc). Primary focus centers on the cases associated with tropical waves, since these waves account for approximately 63% of all Atlantic tropical cyclones. The detection technique illustrates the ability to track these tropical disturbances from near the coast of Africa. Analysis of the pre-tropical cyclone (TC) tracks for these cases depict stages, related to wind speed and precipitation, in the evolution of an easterly wave to tropical cyclone.
Jones, B. (2004).
Influence of Panamanian Wind Jets on the Southeast Intertropical Convergence Zone. Master's thesis, Florida State University, Tallahassee, FL.
Abstract: Gridded QuikSCAT data has been used to show that a strong confluence zone of the Southeast Pacific Intertropical Convergence Zone (SITCZ) existed in 2000 � 2002 during boreal spring, and the Panama wind jet contributes to its variability. Time series analysis of winds off the Gulf of Panama and convergence advection into the Southern Hemisphere (from 80W to 95W) show these winds kept the SE Trades out of the Northern Hemisphere and created a confluent zone in the Southern Hemisphere. A monthly averaged SITCZ is maintained by the deceleration of the SE Trades that flow from warm water toward the equatorial cold tongue, creating a speed convergent zone south of the equator. Images of wind trajectories show zonally orientated SE Trade winds that were deflected from a divergent zone parallel to the coast of South America converge with more meridional Trades over warm waters. Panamanian winds crossed into the Southern Hemisphere to contribute to this convergence. It is hypothesized that this confluent zone can be intensified by the Panamanian winds. In 2002, the SITCZ confluent zone occurred with more intense Panamanian gap flow than the previous two years. Cross equatorial SE Trades wrapped anti-cyclonically around a divergent pocket in the Northern Hemisphere and became southward winds, which allowed the Panamanian winds to enter the Southern Hemisphere and intensify the SITCZ. Variability in the Panamanian winds makes a substantial contribution to the evolution of the SITCZ.
Briggs, K. (2006).
ENSO Event Reproduction: A Comparison of an EOF vs. A Cyclostationary (CSEOF) Approach. Master's thesis, Florida State University, Tallahassee, FL.
Abstract: In past studies, El Niño-Southern Oscillation (ENSO) events have been linked to devastating weather extremes. Climate modeling of ENSO is often dependent on limited records of the pertinent physical variables, thus longer records of these variables is desirable. Noisy signals, such as monthly sea surface temperature, are good candidates for reproduction by several existing auto-regression techniques. Through auto-regression, influential principal component modes are broken down into a series of time points that are each dependent upon an optimal weighting of the surrounding points. Using these unique numerical relationships, a noisy signal can be reproduced by thus processing the leading modes and adding an artificial record of properly distributed noise. Statistical measures of important ENSO regions suggest that the nature of oceanic and atmospheric anomalous events is cyclic with respect to certain timescales; for example, the monthly timescale. To detect ENSO signals in the presence of a varying background noise field, the detection method should take into account the signal's strong phase-locking with this nested variation. Cyclostationary Emperical Orthogonal Functions (CSEOFs) are built upon the idea of nested cycles, unlike traditional EOFs, which incorporate a design that is better detailed for stationary processes. In this study, both EOF and CSEOF modes of a 50-year Pacific SST record are processed using an auto-regression technique, and several sets of artificial SST records are constructed. Appropriate statistical indices are applied to these artificial time series to ensure an acceptable consistency with the real record, and then artificial data is produced using the artificial time series. In all cases, the cyclostationary approach produces more realistic warm ENSO events with respect to timing, strength, and other traits than does the stationary approach. However, both methods produce only a fair representation of cold events, suggesting that further study is necessary for improvement of La Niña modeling. Shorter records of variables such as sea level height and Pacific wind stress anomalies can hinder the usefulness of auto-regression, owing to time point dependence on surrounding points. Using a regression technique to find an evolutionary consistency (i.e. physically consistent patterns) between one of these variables and a variable with a longer record (such as SST) can eliminate this problem. Once a regression relationship is found between two variables, the variable with the shorter record can be re-written to match the time evolution of the variable with the longer record. Here regression, both EOF and CSEOF, is performed on both sea surface temperature and sea level height (a 20-year record), and sea surface temperature and wind stress (a 39-year record). Once the regression relationships are found, artificial SST time series are incorporated in place of the original time series to produce several artificial 50-year SLH and wind stress data sets. 5 Pacific regions are chosen, and statistics and behavior of the artificial sets within these regions are compared to those of the original data. Once again the cyclostationary approach fares better than the stationary. In particular the EOF assumption of cross correlational symmetry fails to capture the direction-dependence of ENSO evolution, causing inconsistent ENSO behavior. This renders an EOF method insufficient for climate modeling and prediction, and implies that a better aim is to incorporate physical cyclic features via a cyclostationary method.
Culin, J. C. (2006).
Wintertime ENSO Variability in Wind Direction Across the Southeast United States. Master's thesis, Florida State University, Tallahassee, FL.
Abstract: Changes in wind direction in association with the phases of the El Niño-Southern Oscillation (ENSO) are identified over the Southeast region of the United States during the winter season (December-February). Wind roses, which depict the percentage of time the wind comes from each direction and can graphically identify the prevailing wind, are computed according to a 12-point compass for 24 stations in the region. Unfolding the wind rose into a 12-bin histogram visually demonstrates the peak frequencies in wind direction during each of the three (warm, cold and neutral) phases of ENSO. Normalized values represent the number of occurrences (counts) per month per ENSO phase, and comparison using percent changes illustrates the differences between phases. Based on similarities in wind direction characteristics, regional topography and results from a formal statistical test, stations are grouped into five geographic regions, with a representative station used to describe conditions in that region. Locations in South Florida show significant differences in the frequencies in wind direction from easterly directions during the cold phase and northerly directions during the warm phase. North Florida stations display cold phase southerly directions, and westerly and northerly directions during the warm phase, both of which are significant for much of the winter. Coastal Atlantic stations reveal winds from westerly directions for both phases. The Piedmont region demonstrates large variability in wind direction due to the influence from the Appalachian Mountains, but generally identifies warm phase and cold phase winds with more zonal influences rather than just from south or north. The Mountainous region also indicates southerly cold phase winds and northerly warm phase winds, but also reveals less of an influence from ENSO or significantly different distributions. Comparisons between observed patterns and those obtained using the NCEP/NCAR Reanalysis data reveal how the model-derived observations resolve the ENSO influence on surface wind direction at selected locations. Overall, resolution of the strength of the signals is not achieved, though the depiction of the general pattern is fair at two of the three locations. Connections between the synoptic flow and surface wind direction are examined via relationships to the storm track associated with the 250 hPa jet stream and sea level pressure patterns during each extreme ENSO phase. Discussion of reasons the NCEP reanalysis illustrates surface wind direction patterns different from those derived from observations is included.
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.
Lombardi, K. C. (2004).
Resolving the Diurnal and Synoptic Variance of Scatterometer Vector Wind Observations. Master's thesis, Florida State University, Tallahassee, FL.
Abstract: Scatterometer observations of vector winds are used to examine the amplitudes of synoptic and diurnal cycles. Scatterometers have the advantage of providing global coverage over water; however, irregular temporal sampling complicates the analyses. A least squares technique is used in determination of the amplitudes and phases of the diurnal and synoptic cycles on spatial scales of 5°, 15°, and 30°. In open ocean areas and regions with sufficient open water, the magnitudes of the diurnal and synoptic cycles are 1.0 ms-1 and 3.5ms-1, respectively. Diurnal amplitudes are highest in the polar regions and close to land surfaces due to sea breeze effects. The fraction of variance explained by the diurnal cycle is greatest near the equator. Synoptic amplitudes are consistently larger downwind of land from storm tracks and in the southern polar region as the time analyzed is during the southern winter season.