COMPASS (Combined OCE MPO ATM Seminar Series) FRIDAY
(11:00 in the Auditorium, unless stated otherwise)
Aug 25 --- NO SEMINAR
Sep 01 --- NO SEMINAR
Sep 08 --- NO SEMINAR
Sep 15 --- NO SEMINAR
Sep 22 --- Student Seminars, Room MSC 343:
Sanchit Mehta (AMP)
"A Comparison of Sea Spume Production Between Fresh and Salt Water in High Wind Conditions"
Under high wind conditions, sea spray (particularly larger particles or sea spume) plays a significant role in the exchange of heat and momentum across the air-sea interface. It is thus critical for the development of tropical cyclones and other extreme marine boundary layer events that occur on a wide range of spatial-temporal scales over different bodies of water (oceans, rivers and lakes). While considerable differences are known to exist in spray formation via bubble production between saltwater and freshwater, herein is described the first laboratory experiment (conducted in the SUSTAIN facility at University of Miami) quantifying both fresh and saline spume droplet production in hurricane wind conditions (U10 around 36-54 m/s), particles with radii 80 µm to 1400 µm were observed. Spume number concentration estimates were found to increase with wind speeds across the board, with comparatively higher magnitudes (1.5-2.5 x) observed for saltwater up to 500 µm radii. Vertical profiles for number and mass concentration were observed to vary linearly with the scaled height, with comparatively steeper slopes for fresh water and more gradual slopes for salt water. While significant differences were observed in the profiles near the surface (concentrations were almost twice as large for salt water than fresh water at higher speeds), the profiles tend to converge as the wind forcing increases. The radius dependency observed for the difference in mass concentration between two media showed bimodality, with a minimum near 700-900 µm particle radii. An exponential decrease was observed in the differences in number concentration between salt and fresh water showed an exponential decrease from 100 µm through 500 µm radii with very little differences observed beyond.
Sep 29 --- Diane Palko (MPO, 1-Hour Student Seminar)
"Northwestern Atlantic Sea Level in a High Resolution Global Coupled Climate Model"
Sea level rise is one of the most often cited consequences of climate change and is presently impacting cities along the east coast of the United States. Typical general circulation models have 1.0° resolution in the ocean, and are unable to resolve features like the Gulf Stream, which may have large effects on regional sea level. As technology has improved we are now capable of running fully coupled climate models with a resolution of 10 km in the ocean. With this enhanced resolution, models can recreate modes of climate variability that are present in observations but missing from climate models with a coarser resolution. Our main goal is to utilize high-resolution (HR, 0.1° in the ocean) coupled model runs of CCSM4 to analyze regional sea surface height (SSH). We characterize the natural variability of SSH along the US coastline using HR runs of CCSM4 with fixed external forcing (i.e. constant present day values of greenhouse gases). The variability of coastal SSH from these runs is compared to reanalysis and unforced, low-resolution (LR, 1.0°) runs of CCSM4. The connection of coastal SSH to basin-wide climate patterns is examined by correlating SSH at coastal locations with SSH, sea surface temperature (SST), and surface kinetic energy at all other locations in the north Atlantic. The HR natural variability model runs are also used to analyze a coupled ocean-atmosphere climate mode in the Gulf of Mexico, which links patterns of precipitation variability to SST and SSH and has potential implications on tidal flooding in the region. We also use the HR models to evaluate the SSH response to fresh water forcing with two hosing experiments (0.1 Sv and 1.0 Sv). A preliminary comparison is made between these HR hosing runs and a typical LR hosing experiment, however these experiments are still ongoing.
Oct 06 --- Student Seminars:
Kayleen McMonigal (MPO)
"Seasonal Variability of the South Indian Ocean Subtropical Gyre Circulation"
The circulation of the south Indian Ocean subtropical gyre has been investigated using data from three hydrographic crossings. These crossings span 22 years and were conducted in different seasons. Here, we investigate whether previously reported changes to the gyre circulation between these hydrographic crossings can be explained by the seasonal variability of the upper 2000m gyre circulation. 34 months of in situ data of the Agulhas Current transport constrain the circulation at the western boundary. The interior gyre geostrophic velocity relative to 2000m is mapped using a combination of altimeter and Argo float profile data designed to suppress unresolved mesoscale variability. These data sources are combined to investigate the cumulative transport of the gyre and the strength of the gyre on a seasonal basis. A seasonal cycle is found, with a maximum gyre strength in austral summer and a seasonal amplitude of less than 10 Sv. The seasonal cycle cannot explain a previously reported gyre strength increase of 17 Sv between 1987 and 2002. Neither the phasing nor the amplitude of the seasonal cycle match that of the Agulhas Current, as determined by a 23 year transport proxy. This suggests that another component of the basin wide flow must be compensating the Agulhas and interior flows on seasonal time scales to maintain mass balance.
Joshua Wadler (MPO) "Convective Downdrafts and Boundary Layer Recovery in Hurricane Earl (2010) Before and During Rapid Intensification"
Mariana Bernardi Bif (OCE)
"Controls on the Fate of Dissolved Organic Carbon Resulting from Net Community Production"
Dissolved organic carbon (DOC) comprises the largest marine reservoir of reduced and reactive carbon at 662 PgC, comparable in size to atmospheric CO2. Most of the fresh DOC released by primary production in the euphotic zone is the "energy fuel" for heterotrophic bacteria, and is rapidly converted back into CO2. The remaining fraction takes longer to be remineralized, resulting in positive net community production (NCP), and ends up exported to the deep ocean via the biological carbon pump. The unsolved processes controlling the production of resistant DOC, and whether this fraction is variable, was the motivation for this work. We simulated upwelling systems of different intensities by combining natural waters, and incubated with microbial communities from the Florida Strait. Main results show that initial availability of inorganic nutrients modify the microbial community structure and magnitude of NCP. A larger fraction of DOC resulting from NCP accumulated in high nutrient incubations (strong upwelling simulation) during autotroph growth. However, this portion was further remineralized by bacteria when the experiment was extended in the dark. In contrast, PO43- limitation in the weak upwelling decreased NCP, producing a small fraction of DOC that was resistant to bacterial remineralization. Upwellings of different intensities, thus, affect the quality of dissolved organic matter, thereby affecting DOC remineralization and residence time in the ocean.
Oct 13 --- NO SEMINAR
Oct 20 --- Student Seminars:
Samantha Ballard (AMP)
"Improving Coastal Wind and Wave Retrievals Using Synthetic Aperture Radar"
An accurate knowledge of wind and wave fields is important for understanding the dynamic coastal environment. Coastal wind data can be difficult to obtain from atmospheric models due to poor resolution near the coast. Satellite-based synthetic aperture radar (SAR) can resolve wind and wave fields quite well, but the well-established SAR wind algorithms for the open oceans are not reliable in coastal areas, where the relationship between wind, waves, and radar image intensities is more complicated. The objective of this work is to develop an improved SAR wind algorithm for coastal areas. During the Coastal Land Air Sea Interaction (CLASI) field experiment in June, 2016, X-band COSMO-SkyMed and C-band Sentinel-1 SAR images were acquired over Monterey Bay, California. An image pair from June 11, 2016 shows a strong image intensity gradient 2.5 kilometers off the coast for which no corresponding wind gradient exists in a Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) model result. Comprehensive investigations were initiated to determine if this feature can be attributed to an oceanic front, an atmospheric front, or something different, and what the correct wind field for the scenario should look like. We discuss how this data interpretation process can be automated and what can be done to avoid incorrect wind retrievals for areas where SAR image intensity variations are dominated by non-atmospheric features.
Heather Hunter (AMP)
"Application of Artificial Neural Networks and Deep Learning to SAR Automatic Target Recognition"
Automatic target recognition (ATR) in synthetic aperture radar (SAR) is concerned with the automated detection and classification of objects of interest, or targets. For oceanographic applications, targets may be ships, icebergs, or pollution, while for land-based applications, targets may be ground and air vehicles or vegetation. To classify a target, the standard SAR ATR system is composed of 3 stages: detection and discrimination of regions of interest, low-level classification or feature extraction, and high-level classification. The high-level classification stage is often performed with clustering techniques, tree-based classifiers, or K-nearest neighbor classifiers, and more recently, artificial neural networks (ANNs). In this study, ANNs and convolutional neural networks (CNNs) are implemented for the classification stage and their results compared. An ANN is a type of supervised machine learning algorithm that learns to classify objects in an image given labeled training data in the form of feature vectors. By contrast, a convolutional neural network (CNN) classifies objects by learning features directly from an input image, without the need for pre-engineered feature vectors. In this study, the features selected for training the ANN include first-order statistics, wavelet coefficients, and Gabor features. Experimental results show that the CNN has a greater classification accuracy than the ANN. This illustrates the potential for CNNs to be used as the classifier for a SAR ATR system, rather than the conventional ANN, and motivates further research into the use of other deep learning algorithms for SAR ATR.
Lisa Nyman (AMP)
"Using Doppler Marine Radar to Detect Surface Currents and Ocean Features"
Oceanographic features such as fronts, small-scale eddies, and internal waves are analyzed more easily when they are represented spatially on a map, not just based on point measurements. During the FLEAT DRI field experiment off the coast of Palau in May 2016, members of the University of Miami remote sensing team operated Helmholtz-Zentrum Geesthacht’s X-band, VV-polarization, Doppler Marine Radar (DMR) onboard the R/V Oceanus. The DMR records real and imaginary parts of the received signals, which can be converted to amplitudes and phases. The difference between the phase of the wave as it returns to the radar and the phase of the immediately subsequent pulse is analyzed and can be used to extract radial current information through the knowledge of the Doppler shift. The data from Palau shows that the DMR registers ship motion, which is the dominant velocity component on a moving ship. During the Inner Shelf DRI field experiment off the California coast in September and October 2017, the DMR was used again to detect ocean features such as internal waves and tidal bores. An algorithm is being developed to process this Doppler information from the rotating antenna on the moving ship in such a way that a temporally-averaged two-dimensional vector velocity field is obtained with a spatial resolution of 370 m × 370 m. Although still under development, this process seems to be a promising new way to measure two-dimensional surface current fields from a ship.
Oct 27 --- Student Seminars, Room MSC 343:
Luo Bingkun (MPO)
Samantha Kramer (MPO)
Rebecca Evans (MPO)
Nov 03 --- Student Seminars, Room MSC 343:
Andrew Smith (MPO)
Breanna Zavadoff (MPO)
Shun-Nan Wu (MPO)
Nov 10 --- Student Seminars, Room MSC 343:
Gedun Chen (MAC)
Hanjing Dai (AMP)
Alexis Denton (AMP)
Nov 17 --- Student Seminars, Room MSC 343:
Nektaria Ntaganou (MPO)
Tiago Bilo (MPO)
Gregory Koman (MPO)
Nov 24 --- THANKSGIVING BREAK
Dec 01 --- Student Seminars, Room MSC 343:
Anne Barkley (ATM)
Lisa Bucci (ATM)
Kurt Hansen (ATM)
Szandra Peters (MPO)
Dec 08 --- Eleanor Middlemas (MPO, 1-Hour Student Seminar)