229 results on '"MINNETT, P."'
Search Results
2. Improving Atmospheric Correction Algorithms for Sea Surface Skin Temperature Retrievals from Moderate-Resolution Imaging Spectroradiometer Using Machine Learning Methods
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Bingkun Luo, Peter J. Minnett, and Chong Jia
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sea surface skin temperature ,atmospheric correction algorithms ,machine learning ,Science - Abstract
Satellite-retrieved sea-surface skin temperature (SSTskin) is essential for many Near-Real-Time studies. This study aimed to assess the potential to improve the accuracy of satellite-based SSTskin retrieval in the Caribbean region by using atmospheric correction algorithms based on four readily available machine learning (ML) approaches: eXtreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), Random Forest (RF), and the Artificial Neural Network (ANN). The ML models were trained on an extensive dataset comprising in situ SST measurements and atmospheric state parameters obtained from satellite products, reanalyzed datasets, research cruises, surface moorings, and drifting buoys. The benefits and shortcomings of various ML methods were assessed through comparisons with withheld in situ measurements. The results demonstrate that the ML-based algorithms achieve promising accuracy, with mean biases within 0.07 K when compared with the buoy data and ranging from −0.107 K to 0.179 K relative to the ship-derived SSTskin data. Notably, both XGBoost and RF stand out for their superior correlation and efficacy in the statistical results of validation. The improved SSTskin derived using the ML-based algorithms could enhance our understanding of vital oceanic and atmospheric characteristics and have the potential to reduce uncertainty in oceanographic, meteorological, and climate research.
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- 2024
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3. Characteristics of R2019 Processing of MODIS Sea Surface Temperature at High Latitudes
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Chong Jia, Peter J. Minnett, and Malgorzata Szczodrak
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MODIS ,sea surface temperature ,error characteristics ,algorithm ,atmospheric inversions ,Arctic ,Science - Abstract
Satellite remote sensing is the best way to derive sea surface skin temperature (SSTskin) in the Arctic. However, as surface temperature retrieval algorithms in the infrared (IR) part of the electromagnetic spectrum are designed to compensate for atmospheric effects mainly due to water vapor, MODIS SSTskin retrievals have larger uncertainties at high latitudes where the atmosphere is very dry and cold, which is an extreme in the distribution of global conditions. MODIS R2019 SSTskin fields are currently derived using latitudinally and monthly dependent algorithm coefficients, including an additional band above 60°N to better represent the effects of Arctic atmospheres. However, the R2019 processing of MODIS SSTskin still has some unrevealed error characteristics. This study uses 21 years (2002–2022) of collocated, simultaneous satellite brightness temperature (BT) data from Aqua MODIS and in situ buoy-measured subsurface temperature data from iQuam for validation. Unlike elsewhere over the oceans, the 11 μm and 12 μm BT differences are poorly related to the column water vapor at high latitudes, resulting in poor atmospheric water vapor correction. Anomalous BT difference signals are identified, caused by the temperature and humidity inversions in the lower troposphere, which are especially significant during the summer. Although the existence of negative BT differences is physically reasonable, this makes the retrieval algorithm lose its effectiveness. Moreover, the statistics of the MODIS SSTskin data when compared with the iQuam buoy temperature data show large differences (in terms of mean and standard deviation) for the matchups at the Northern Atlantic and Pacific sides of the Arctic due to the disparity of in situ measurements and distinct surface and vertical atmospheric conditions. Therefore, it is necessary to further improve the retrieval algorithms to obtain more accurate MODIS SSTskin data to study surface ocean processes and climate change in the Arctic.
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- 2024
- Full Text
- View/download PDF
4. Evaluation of Summertime Passive Microwave and Reanalysis Sea‐Ice Concentration in the Central Arctic
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Kexin Song and Peter J. Minnett
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Arctic sea ice ,passive microwave ,reanalysis ,MODIS ,Astronomy ,QB1-991 ,Geology ,QE1-996.5 - Abstract
Abstract Passive microwave (PM) observations have been used to monitor ice retreat in the Arctic. However, various PM sea ice concentration (SIC) algorithms are prone to underestimate ice fraction during summer. We evaluated the accuracy of 2002–2019 low SICs in the Central Arctic Ocean of four PM products from the University of Bremen, the National Snow and Ice Data Center (NSIDC), and the Ocean and Sea Ice Satellite Application Facility (OSI SAF), and two reanalysis data sets from the fifth generation of European ReAnalysis (ERA5) and the Modern‐Era Retrospective analysis for Research and Applications, Version 2 (MERRA‐2). Three reference fields were used: (a) Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) true‐color composites, (b) MODIS sea ice extent, and (c) multi‐product ensemble (MPE‐SIC) comprising the median of collocated SIC estimates. Our results indicate SICs derived from the Advanced Microwave Scanning Radiometer ‐ Earth Observing System (AMSR‐E) and the Advanced Microwave Scanning Radiometer 2 (AMSR2) high frequency channels have the best accuracy. Reanalysis SICs indicate almost identical patterns as their remote sensing inputs. The assessment shows that the Bremen (+1.06%) and NSIDC (+0.99%) SICs are higher than the median field, while the OSI‐401 (−6.65%) and OSI‐408 (−4.64%) have negative mean deviations. The mean error of MODIS‐derived SIC (−0.80%) is smaller than PM SICs. These small mean values belie wide distributions of values. The correlation coefficients of pairs of time series of Low sea‐Ice Concentration Index range from 0.37 to 0.96.
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- 2024
- Full Text
- View/download PDF
5. The Magnetics Information Consortium (MagIC) Data Repository: Successes and Continuing Challenges
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Jarboe, Nicholas, Minnett, Rupert, Constable, Catherine, Koppers, Anthony, and Tauxe, Lisa
- Abstract
MagIC (earthref.org/MagIC) is an organization dedicated to improving research capacity in the Earth and Ocean sciences by maintaining an open community digital data archive for rock and paleomagnetic data with portals that allow users access to archive, search, visualize, download, and combine these versioned datasets. We are a signatory of the Coalition for Publishing Data in the Earth and Space Sciences (COPDESS)'s Enabling FAIR Data Commitment Statement and an approved repository for the Nature set of journals. We have been in collaboration with EarthCube's GeoCodes data search portal, adding schema.org/JSON-LD headers to our data set landing pages and suggesting extensions to schema.org when needed. Collaboration with the European Plate Observing System (EPOS)'s Thematic Core Service Multi-scale laboratories (TCS MSL) is ongoing with MagIC sending its contributions' metadata to TCS MSL via DataCite records.Improving and updating our data repository to meet the demands of the quickly changing landscape of data archival, retrieval, and interoperability is a challenging proposition. Most journals now require data to be archived in a "FAIR" repository, but the exact specifications of FAIR are still solidifying. Some journals vet and have their own list of accepted repositories while others rely on other organizations to investigate and certify repositories. As part of the COPDESS group at Earth Science Information Partners (ESIP), we have been and will continue to be part of the discussion on the needed and desired features for acceptable data repositories.We are actively developing our software and systems to meet the needs of our scientific community. Some current issues we are confronting are: developing workflows with journals on how to publish the journal article and data in MagIC simultaneously, sustainability of data repository funding especially in light of the greater demands on them due to data policy changes at journals, and how to best share and expose metadata about our data holdings to organizations such as EPOS, EarthCube, and Google.
- Published
- 2021
6. Assessment of Accuracy of Moderate-Resolution Imaging Spectroradiometer Sea Surface Temperature at High Latitudes Using Saildrone Data
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Chong Jia, Peter J. Minnett, and Malgorzata Szczodrak
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MODIS ,sea surface skin temperature ,Saildrone ,validation ,Arctic ,Science - Abstract
The infrared (IR) satellite remote sensing of sea surface skin temperature (SSTskin) is challenging in the northern high-latitude region, especially in the Arctic because of its extreme environmental conditions, and thus the accuracy of SSTskin retrievals is questionable. Several Saildrone uncrewed surface vehicles were deployed at the Pacific side of the Arctic in 2019, and two of them, SD-1036 and SD-1037, were equipped with a pair of IR pyrometers on the deck, whose measurements have been shown to be useful in the derivation of SSTskin with sufficient accuracy for scientific applications, providing an opportunity to validate satellite SSTskin retrievals. This study aims to assess the accuracy of MODIS-retrieved SSTskin from both Aqua and Terra satellites by comparisons with collocated Saildrone-derived SSTskin data. The mean difference in SSTskin from the SD-1036 and SD-1037 measurements is ~0.4 K, largely resulting from differences in the atmospheric conditions experienced by the two Saildrones. The performance of MODIS on Aqua and Terra in retrieving SSTskin is comparable. Negative brightness temperature (BT) differences between 11 μm and 12 μm channels are identified as being physically based, but are removed from the analyses as they present anomalous conditions for which the atmospheric correction algorithm is not suited. Overall, the MODIS SSTskin retrievals show negative mean biases, −0.234 K for Aqua and −0.295 K for Terra. The variations in the retrieval inaccuracies show an association with diurnal warming events in the upper ocean from long periods of sunlight in the Arctic. Also contributing to inaccuracies in the retrieval is the surface emissivity effect in BT differences characterized by the Emissivity-introduced BT difference (EΔBT) index. This study demonstrates the characteristics of MODIS-retrieved SSTskin in the Arctic, at least at the Pacific side, and underscores that more in situ SSTskin data at high latitudes are needed for further error identification and algorithm development of IR SSTskin.
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- 2024
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7. FluxSat: Measuring the Ocean-Atmosphere Turbulent Exchange of Heat and Moisture from Space
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Gentemann, Chelle L, Clayson, Carol Anne, Brown, Shannon, Lee, Tong, Parfitt, Rhys, Farrar, J Thomas, Bourassa, Mark, Minnett, Peter J, Seo, Hyodae, Gille, Sarah T, and Zlotnicki, Victor
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air-sea interactions ,mesoscale ,fluxes ,Classical Physics ,Physical Geography and Environmental Geoscience ,Geomatic Engineering - Abstract
Recent results using wind and sea surface temperature data from satellites and high-resolution coupled models suggest that mesoscale ocean–atmosphere interactions affect the locations and evolution of storms and seasonal precipitation over continental regions such as the western US and Europe. The processes responsible for this coupling are difficult to verify due to the paucity of accurate air–sea turbulent heat and moisture flux data. These fluxes are currently derived by combining satellite measurements that are not coincident and have differing and relatively low spatial resolutions, introducing sampling errors that are largest in regions with high spatial and temporal variability. Observational errors related to sensor design also contribute to increased uncertainty. Leveraging recent advances in sensor technology, we here describe a satellite mission concept, FluxSat, that aims to simultaneously measure all variables necessary for accurate estimation of ocean–atmosphere turbulent heat and moisture fluxes and capture the effect of oceanic mesoscale forcing. Sensor design is expected to reduce observational errors of the latent and sensible heat fluxes by almost 50%. FluxSat will improve the accuracy of the fluxes at spatial scales critical to understanding the coupled ocean–atmosphere boundary layer system, providing measurements needed to improve weather forecasts and climate model simulations.
- Published
- 2020
8. The Magnetics Information Consortium (MagIC) Data Repository: Successes and Continuing Challenges
- Author
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Jarboe, Nicholas, Minnett, Rupert, Constable, Catherine, Koppers, Anthony, and Tauxe, Lisa
- Abstract
MagIC (earthref.org/MagIC) is an organization dedicated to improving research capacity in the Earth and Ocean sciences by maintaining an open community digital data archive for rock and paleomagnetic data with portals that allow users access to archive, search, visualize, download, and combine these versioned datasets. We are a signatory of the Coalition for Publishing Data in the Earth and Space Sciences (COPDESS)'s Enabling FAIR Data Commitment Statement and an approved repository for the Nature set of journals. We have been in collaboration with EarthCube's GeoCodes data search portal, adding schema.org/JSON-LD headers to our data set landing pages and suggesting extensions to schema.org when needed. Collaboration with the European Plate Observing System (EPOS)'s Thematic Core Service Multi-scale laboratories (TCS MSL) is ongoing with MagIC sending its contributions' metadata to TCS MSL via DataCite records.Improving and updating our data repository to meet the demands of the quickly changing landscape of data archival, retrieval, and interoperability is a challenging proposition. Most journals now require data to be archived in a "FAIR" repository, but the exact specifications of FAIR are still solidifying. Some journals vet and have their own list of accepted repositories while others rely on other organizations to investigate and certify repositories. As part of the COPDESS group at Earth Science Information Partners (ESIP), we have been and will continue to be part of the discussion on the needed and desired features for acceptable data repositories.We are actively developing our software and systems to meet the needs of our scientific community. Some current issues we are confronting are: developing workflows with journals on how to publish the journal article and data in MagIC simultaneously, sustainability of data repository funding especially in light of the greater demands on them due to data policy changes at journals, and how to best share and expose metadata about our data holdings to organizations such as EPOS, EarthCube, and Google.
- Published
- 2020
9. Effects of the Hunga Tonga‐Hunga Ha'apai Eruption on MODIS‐Retrieved Sea Surface Temperatures
- Author
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Chong Jia and Peter J. Minnett
- Subjects
Tonga volcanic eruption ,Aerosol ,MODIS ,Sea surface temperature ,Geophysics. Cosmic physics ,QC801-809 - Abstract
Abstract The eruption of Hunga Tonga‐Hunga Ha'apai (HTHH) volcano on 15 January 2022 injected a great amount of H2O and a moderate amount of SO2 into the stratosphere, producing a pronounced and persistent sulfate aerosol layer centered around the mid‐stratosphere, mostly confined to Southern Hemisphere (SH) tropics. These aerosols affect the Moderate Resolution Imaging Spectroradiometer (MODIS) retrievals of sea surface temperature (SST) where negative biases reached −0.3 K and an annual mean of −0.1 K north of 40°S in the SH. The spatial and temporal evolutions of MODIS SST anomalies are presented. Radiative transfer simulations demonstrate the aerosol effect on MODIS SST retrievals by causing an additional brightness temperature (BT) deficit at 11 μm and a reduction in BT differences since the characteristic of spectral attenuation between 11 and 12 μm is opposite to that of H2O. A correction for HTHH aerosol effects in the retrieval algorithm is therefore desirable.
- Published
- 2023
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- View/download PDF
10. Air-Sea Fluxes With a Focus on Heat and Momentum
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Cronin, Meghan F, Gentemann, Chelle L, Edson, James, Ueki, Iwao, Bourassa, Mark, Brown, Shannon, Clayson, Carol Anne, Fairall, Chris W, Farrar, J Thomas, Gille, Sarah T, Gulev, Sergey, Josey, Simon A, Kato, Seiji, Katsumata, Masaki, Kent, Elizabeth, Krug, Marjolaine, Minnett, Peter J, Parfitt, Rhys, Pinker, Rachel T, Jr, Stackhouse Paul W, Swart, Sebastiaan, Tomita, Hiroyuki, Vandemark, Douglas, AWeller, Robert, Yoneyama, Kunio, Yu, Lisan, and Zhang, Dongxiao
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air-sea heat flux ,latent heat flux ,surface radiation ,ocean wind stress ,autonomous surface vehicle ,OceanSITES ,ICOADS ,satellite-based ocean monitoring system ,Oceanography ,Ecology - Published
- 2019
11. Ocean Warm Skin Signals Observed by Saildrone at High Latitudes
- Author
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Chong Jia and Peter J. Minnett
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Saildrone ,sea surface temperature ,warm skin ,Arctic ,Geophysics. Cosmic physics ,QC801-809 - Abstract
Abstract The existence of a cool sea surface skin layer in the global ocean during both day and night is generally recognized. However, a warm skin should be present if the total surface net heat flux (Qnet) were to be from the atmosphere into ocean. Saildrone, an advanced uncrewed surface vehicle, has been shown to be able to provide sufficiently accurate sea skin temperature (SSTskin) and subsurface temperature (SSTdepth) data at high latitudes. Using those SST data along with meteorological parameters from a Saildrone deployed in the Arctic in the summer of 2019, some warm skin layers were identified due to the Qnet gain resulting from the combined effect of positive air‐sea temperature difference, humid surface air and cloudy skies. Furthermore, most warm skins here were found during and shortly after rainfall events. It is essential to incorporate the ability to simulate warm skin layers in the present cool skin models.
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- 2023
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12. EUREC4A
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B. Stevens, S. Bony, D. Farrell, F. Ament, A. Blyth, C. Fairall, J. Karstensen, P. K. Quinn, S. Speich, C. Acquistapace, F. Aemisegger, A. L. Albright, H. Bellenger, E. Bodenschatz, K.-A. Caesar, R. Chewitt-Lucas, G. de Boer, J. Delanoë, L. Denby, F. Ewald, B. Fildier, M. Forde, G. George, S. Gross, M. Hagen, A. Hausold, K. J. Heywood, L. Hirsch, M. Jacob, F. Jansen, S. Kinne, D. Klocke, T. Kölling, H. Konow, M. Lothon, W. Mohr, A. K. Naumann, L. Nuijens, L. Olivier, R. Pincus, M. Pöhlker, G. Reverdin, G. Roberts, S. Schnitt, H. Schulz, A. P. Siebesma, C. C. Stephan, P. Sullivan, L. Touzé-Peiffer, J. Vial, R. Vogel, P. Zuidema, N. Alexander, L. Alves, S. Arixi, H. Asmath, G. Bagheri, K. Baier, A. Bailey, D. Baranowski, A. Baron, S. Barrau, P. A. Barrett, F. Batier, A. Behrendt, A. Bendinger, F. Beucher, S. Bigorre, E. Blades, P. Blossey, O. Bock, S. Böing, P. Bosser, D. Bourras, P. Bouruet-Aubertot, K. Bower, P. Branellec, H. Branger, M. Brennek, A. Brewer, P.-E. Brilouet, B. Brügmann, S. A. Buehler, E. Burke, R. Burton, R. Calmer, J.-C. Canonici, X. Carton, G. Cato Jr., J. A. Charles, P. Chazette, Y. Chen, M. T. Chilinski, T. Choularton, P. Chuang, S. Clarke, H. Coe, C. Cornet, P. Coutris, F. Couvreux, S. Crewell, T. Cronin, Z. Cui, Y. Cuypers, A. Daley, G. M. Damerell, T. Dauhut, H. Deneke, J.-P. Desbios, S. Dörner, S. Donner, V. Douet, K. Drushka, M. Dütsch, A. Ehrlich, K. Emanuel, A. Emmanouilidis, J.-C. Etienne, S. Etienne-Leblanc, G. Faure, G. Feingold, L. Ferrero, A. Fix, C. Flamant, P. J. Flatau, G. R. Foltz, L. Forster, I. Furtuna, A. Gadian, J. Galewsky, M. Gallagher, P. Gallimore, C. Gaston, C. Gentemann, N. Geyskens, A. Giez, J. Gollop, I. Gouirand, C. Gourbeyre, D. de Graaf, G. E. de Groot, R. Grosz, J. Güttler, M. Gutleben, K. Hall, G. Harris, K. C. Helfer, D. Henze, C. Herbert, B. Holanda, A. Ibanez-Landeta, J. Intrieri, S. Iyer, F. Julien, H. Kalesse, J. Kazil, A. Kellman, A. T. Kidane, U. Kirchner, M. Klingebiel, M. Körner, L. A. Kremper, J. Kretzschmar, O. Krüger, W. Kumala, A. Kurz, P. L'Hégaret, M. Labaste, T. Lachlan-Cope, A. Laing, P. Landschützer, T. Lang, D. Lange, I. Lange, C. Laplace, G. Lavik, R. Laxenaire, C. Le Bihan, M. Leandro, N. Lefevre, M. Lena, D. Lenschow, Q. Li, G. Lloyd, S. Los, N. Losi, O. Lovell, C. Luneau, P. Makuch, S. Malinowski, G. Manta, E. Marinou, N. Marsden, S. Masson, N. Maury, B. Mayer, M. Mayers-Als, C. Mazel, W. McGeary, J. C. McWilliams, M. Mech, M. Mehlmann, A. N. Meroni, T. Mieslinger, A. Minikin, P. Minnett, G. Möller, Y. Morfa Avalos, C. Muller, I. Musat, A. Napoli, A. Neuberger, C. Noisel, D. Noone, F. Nordsiek, J. L. Nowak, L. Oswald, D. J. Parker, C. Peck, R. Person, M. Philippi, A. Plueddemann, C. Pöhlker, V. Pörtge, U. Pöschl, L. Pologne, M. Posyniak, M. Prange, E. Quiñones Meléndez, J. Radtke, K. Ramage, J. Reimann, L. Renault, K. Reus, A. Reyes, J. Ribbe, M. Ringel, M. Ritschel, C. B. Rocha, N. Rochetin, J. Röttenbacher, C. Rollo, H. Royer, P. Sadoulet, L. Saffin, S. Sandiford, I. Sandu, M. Schäfer, V. Schemann, I. Schirmacher, O. Schlenczek, J. Schmidt, M. Schröder, A. Schwarzenboeck, A. Sealy, C. J. Senff, I. Serikov, S. Shohan, E. Siddle, A. Smirnov, F. Späth, B. Spooner, M. K. Stolla, W. Szkółka, S. P. de Szoeke, S. Tarot, E. Tetoni, E. Thompson, J. Thomson, L. Tomassini, J. Totems, A. A. Ubele, L. Villiger, J. von Arx, T. Wagner, A. Walther, B. Webber, M. Wendisch, S. Whitehall, A. Wiltshire, A. A. Wing, M. Wirth, J. Wiskandt, K. Wolf, L. Worbes, E. Wright, V. Wulfmeyer, S. Young, C. Zhang, D. Zhang, F. Ziemen, T. Zinner, and M. Zöger
- Subjects
Environmental sciences ,GE1-350 ,Geology ,QE1-996.5 - Abstract
The science guiding the EUREC4A campaign and its measurements is presented. EUREC4A comprised roughly 5 weeks of measurements in the downstream winter trades of the North Atlantic – eastward and southeastward of Barbados. Through its ability to characterize processes operating across a wide range of scales, EUREC4A marked a turning point in our ability to observationally study factors influencing clouds in the trades, how they will respond to warming, and their link to other components of the earth system, such as upper-ocean processes or the life cycle of particulate matter. This characterization was made possible by thousands (2500) of sondes distributed to measure circulations on meso- (200 km) and larger (500 km) scales, roughly 400 h of flight time by four heavily instrumented research aircraft; four global-class research vessels; an advanced ground-based cloud observatory; scores of autonomous observing platforms operating in the upper ocean (nearly 10 000 profiles), lower atmosphere (continuous profiling), and along the air–sea interface; a network of water stable isotopologue measurements; targeted tasking of satellite remote sensing; and modeling with a new generation of weather and climate models. In addition to providing an outline of the novel measurements and their composition into a unified and coordinated campaign, the six distinct scientific facets that EUREC4A explored – from North Brazil Current rings to turbulence-induced clustering of cloud droplets and its influence on warm-rain formation – are presented along with an overview of EUREC4A's outreach activities, environmental impact, and guidelines for scientific practice. Track data for all platforms are standardized and accessible at https://doi.org/10.25326/165 (Stevens, 2021), and a film documenting the campaign is provided as a video supplement.
- Published
- 2021
- Full Text
- View/download PDF
13. Help a Sista Out: Black Women Doctoral Students' Use of Peer Mentorship as an Act of Resistance
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Minnett, Jari L., James-Gallaway, ArCasia D., and Owens, Devean R.
- Abstract
Many Black women doctoral students entering and persisting through graduate study lack the affirmation, community, and resources necessary to confidently assert themselves as members of the academy. These barriers make it especially difficult for Black women to effectively navigate doctoral programs that privilege and normalize elite white male experiences. Using Black feminism as the conceptual lens, this manuscript presents a burgeoning peer mentorship framework of Black women doctoral students attending a predominantly white institution through a collective Black feminist autoethnography. This model highlights our strategy for not only surviving the academy, but also resisting manifestations of white heteropatriarchal violence within academia. In contrast to more common and formal faculty-student mentorship models, we engage an emergent, horizontal peer mentorship framework, comprised of three tenets: radical coping, communal sista scholarship, and the cultivation of an authentic holistic self.
- Published
- 2019
14. PmagPy: Software package for paleomagnetic data analysis and a bridge to the Magnetics Information Consortium (MagIC) Database
- Author
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Tauxe, L, Shaar, R, Jonestrask, L, Swanson‐Hysell, NL, Minnett, R, Koppers, AAP, Constable, CG, Jarboe, N, Gaastra, K, and Fairchild, L
- Subjects
Networking and Information Technology R&D (NITRD) ,Magnetics Information Consortium ,MagIC database ,PmagPy software package ,paleomagnetic and rock magnetic database ,Physical Sciences ,Earth Sciences ,Geochemistry & Geophysics - Abstract
The Magnetics Information Consortium (MagIC) database provides an archive with a flexible data model for paleomagnetic and rock magnetic data. The PmagPy software package is a cross-platform and open-source set of tools written in Python for the analysis of paleomagnetic data that serves as one interface to MagIC, accommodating various levels of user expertise. PmagPy facilitates thorough documentation of sampling, measurements, data sets, visualization, and interpretation of paleomagnetic and rock magnetic experimental data. Although not the only route into the MagIC database, PmagPy makes preparation of newly published data sets for contribution to MagIC as a byproduct of normal data analysis and allows manipulation as well as reanalysis of data sets downloaded from MagIC with a single software package. The graphical user interface (GUI), Pmag GUI enables use of much of PmagPy's functionality, but the full capabilities of PmagPy extend well beyond that. Over 400 programs and functions can be called from the command line interface mode, or from within the interactive Jupyter notebooks. Use of PmagPy within a notebook allows for documentation of the workflow from the laboratory to the production of each published figure or data table, making research results fully reproducible. The PmagPy design and its development using GitHub accommodates extensions to its capabilities through development of new tools by the user community. Here we describe the PmagPy software package and illustrate the power of data discovery and reuse through a reanalysis of published paleointensity data which illustrates how the effectiveness of selection criteria can be tested.
- Published
- 2016
15. Machine-Learning Classification of SAR Remotely-Sensed Sea-Surface Petroleum Signatures—Part 1: Training and Testing Cross Validation
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Gustavo de Araújo Carvalho, Peter J. Minnett, Nelson F. F. Ebecken, and Luiz Landau
- Subjects
oil slicks ,oil spills ,oil seeps ,look-alike slicks ,ocean remote sensing ,satellite ,Science - Abstract
Sea-surface petroleum pollution is observed as “oil slicks” (i.e., “oil spills” or “oil seeps”) and can be confused with “look-alike slicks” (i.e., environmental phenomena, such as low-wind speed, upwelling conditions, chlorophyll, etc.) in synthetic aperture radar (SAR) measurements, the most proficient satellite sensor to detect mineral oil on the sea surface. Even though machine learning (ML) has become widely used to classify remotely-sensed petroleum signatures, few papers have been published comparing various ML methods to distinguish spills from look-alikes. Our research fills this gap by comparing and evaluating six traditional techniques: simple (naive Bayes (NB), K-nearest neighbor (KNN), decision trees (DT)) and advanced (random forest (RF), support vector machine (SVM), artificial neural network (ANN)) applied to different combinations of satellite-retrieved attributes. 36 ML algorithms were used to discriminate “ocean-slick signatures” (spills versus look-alikes) with ten-times repeated random subsampling cross validation (70-30 train-test partition). Our results found that the best algorithm (ANN: 90%) was >20% more effective than the least accurate one (DT: ~68%). Our empirical ML observations contribute to both scientific ocean remote-sensing research and to oil and gas industry activities, in that: (i) most techniques were superior when morphological information and Meteorological and Oceanographic (MetOc) parameters were included together, and less accurate when these variables were used separately; (ii) the algorithms with the better performance used more variables (without feature selection), while lower accuracy algorithms were those that used fewer variables (with feature selection); (iii) we created algorithms more effective than those of benchmark-past studies that used linear discriminant analysis (LDA: ~85%) on the same dataset; and (iv) accurate algorithms can assist in finding new offshore fossil fuel discoveries (i.e., misclassification reduction).
- Published
- 2022
- Full Text
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16. Relative Merits of Optimal Estimation and Non-Linear Retrievals of Sea-Surface Temperature from MODIS
- Author
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Malgorzata D. Szczodrak and Peter J. Minnett
- Subjects
sea surface temperature retrievals ,MODIS ,Optimal Estimation vs. non-linear ,Science - Abstract
We compared the results of an Optimal Estimation (OE) based approach for the retrieval of the skin sea surface temperature (SSTskin) with those of the traditional non-linear sea surface temperature (NLSST) algorithm. The retrievals were from radiance measurements in two infrared channels of the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA satellite Aqua. The OE used a reduced state vector of SST and total column water vapor (TCWV). The SST and atmospheric profiles of temperature and humidity from ERA5 provided prior knowledge, and we made reasonable assumptions about the variance of these fields. An atmospheric radiative transfer model was used as the forward model to simulate the MODIS measurements. The performances of the retrieval approaches were assessed by comparison with in situ measurements. We found that the OESST reduces the satellite–in situ bias, but mostly for retrievals with an already small bias between in situ and the prior SST. The OE approach generally fails to improve the SST retrieval when that difference is large. In such cases, the NLSST often provides a better estimate of the SST than the OE. The OESST also underperforms NLSST in areas that include large horizontal SST gradients.
- Published
- 2022
- Full Text
- View/download PDF
17. The Social Ecology Research Project, 1988-1991. Final Report.
- Author
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Texas Univ., Dallas. School of Human Development., Dallas Independent School District, TX., Coleman, J. Michael, and Minnett, Ann M.
- Abstract
This report describes the Social Ecology Research Project, which assessed the foundations of personal-social competence in children with mental handicaps (MH). Children with mild MH (n=1,200) and their normally achieving peers (n=2,500), all ages 8-14, were studied over 3 years. Students were assessed in resource and regular classrooms, and information was obtained concerning perceptions of the child's competence within the family and other nonschool groups such as scouts. Social competence ratings, which were obtained from the child, teachers, parents, and from observation, covered self-concept, social status among peers, self-attributions, level of social cognition, loneliness, social affiliations, and social interactions. Academic information was also obtained. The following papers are appended: "Learning Disabilities and Social Competence: A Social Ecological Perspective" (J. Michael Coleman and Ann M. Minnett) and "Similarities in the Social Competencies of Learning Disabled and Low-Achieving Elementary School Children" (J. Michael Coleman et. al). A list of 28 papers and dissertations completed by project staff is included, along with questionnaires and survey instruments. (Contains 69 references total.) (SW)
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- 1991
18. Oil Spills or Look-Alikes? Classification Rank of Surface Ocean Slick Signatures in Satellite Data
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Gustavo de Araújo Carvalho, Peter J. Minnett, Nelson F. F. Ebecken, and Luiz Landau
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remote sensing ,synthetic aperture radar (SAR) ,microwave sensors ,optical sensors ,image processing ,linear discriminant analysis (LDA) ,Science - Abstract
Linear discriminant analysis (LDA) is a mathematically robust multivariate data analysis approach that is sometimes used for surface oil slick signature classification. Our goal is to rank the effectiveness of LDAs to differentiate oil spills from look-alike slicks. We explored multiple combinations of (i) variables (size information, Meteorological-Oceanographic (metoc), geo-location parameters) and (ii) data transformations (non-transformed, cube root, log10). Active and passive satellite-based measurements of RADARSAT, QuikSCAT, AVHRR, SeaWiFS, and MODIS were used. Results from two experiments are reported and discussed: (i) an investigation of 60 combinations of several attributes subjected to the same data transformation and (ii) a survey of 54 other data combinations of three selected variables subjected to different data transformations. In Experiment 1, the best discrimination was reached using ten cube-transformed attributes: ~85% overall accuracy using six pieces of size information, three metoc variables, and one geo-location parameter. In Experiment 2, two combinations of three variables tied as the most effective: ~81% of overall accuracy using area (log transformed), length-to-width ratio (log- or cube-transformed), and number of feature parts (non-transformed). After verifying the classification accuracy of 114 algorithms by comparing with expert interpretations, we concluded that applying different data transformations and accounting for metoc and geo-location attributes optimizes the accuracies of binary classifiers (oil spill vs. look-alike slicks) using the simple LDA technique.
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- 2021
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19. Observational Needs of Sea Surface Temperature
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Anne G. O’Carroll, Edward M. Armstrong, Helen M. Beggs, Marouan Bouali, Kenneth S. Casey, Gary K. Corlett, Prasanjit Dash, Craig J. Donlon, Chelle L. Gentemann, Jacob L. Høyer, Alexander Ignatov, Kamila Kabobah, Misako Kachi, Yukio Kurihara, Ioanna Karagali, Eileen Maturi, Christopher J. Merchant, Salvatore Marullo, Peter J. Minnett, Matthew Pennybacker, Balaji Ramakrishnan, RAAJ Ramsankaran, Rosalia Santoleri, Swathy Sunder, Stéphane Saux Picart, Jorge Vázquez-Cuervo, and Werenfrid Wimmer
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sea surface temperature ,observations ,GHRSST ,satellite ,in situ ,Fiducial Reference Measurements ,Science ,General. Including nature conservation, geographical distribution ,QH1-199.5 - Abstract
Sea surface temperature (SST) is a fundamental physical variable for understanding, quantifying and predicting complex interactions between the ocean and the atmosphere. Such processes determine how heat from the sun is redistributed across the global oceans, directly impacting large- and small-scale weather and climate patterns. The provision of daily maps of global SST for operational systems, climate modeling and the broader scientific community is now a mature and sustained service coordinated by the Group for High Resolution Sea Surface Temperature (GHRSST) and the CEOS SST Virtual Constellation (CEOS SST-VC). Data streams are shared, indexed, processed, quality controlled, analyzed, and documented within a Regional/Global Task Sharing (R/GTS) framework, which is implemented internationally in a distributed manner. Products rely on a combination of low-Earth orbit infrared and microwave satellite imagery, geostationary orbit infrared satellite imagery, and in situ data from moored and drifting buoys, Argo floats, and a suite of independent, fully characterized and traceable in situ measurements for product validation (Fiducial Reference Measurements, FRM). Research and development continues to tackle problems such as instrument calibration, algorithm development, diurnal variability, derivation of high-quality skin and depth temperatures, and areas of specific interest such as the high latitudes and coastal areas. In this white paper, we review progress versus the challenges we set out 10 years ago in a previous paper, highlight remaining and new research and development challenges for the next 10 years (such as the need for sustained continuity of passive microwave SST using a 6.9 GHz channel), and conclude with needs to achieve an integrated global high-resolution SST observing system, with focus on satellite observations exploited in conjunction with in situ SSTs. The paper directly relates to the theme of Data Information Systems and also contributes to Ocean Observing Governance and Ocean Technology and Networks within the OceanObs2019 objectives. Applications of SST contribute to all the seven societal benefits, covering Discovery; Ecosystem Health & Biodiversity; Climate Variability & Change; Water, Food, & Energy Security; Pollution & Human Health; Hazards and Maritime Safety; and the Blue Economy.
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- 2019
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20. Global in situ Observations of Essential Climate and Ocean Variables at the Air–Sea Interface
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Luca R. Centurioni, Jon Turton, Rick Lumpkin, Lancelot Braasch, Gary Brassington, Yi Chao, Etienne Charpentier, Zhaohui Chen, Gary Corlett, Kathleen Dohan, Craig Donlon, Champika Gallage, Verena Hormann, Alexander Ignatov, Bruce Ingleby, Robert Jensen, Boris A. Kelly-Gerreyn, Inga M. Koszalka, Xiaopei Lin, Eric Lindstrom, Nikolai Maximenko, Christopher J. Merchant, Peter Minnett, Anne O’Carroll, Theresa Paluszkiewicz, Paul Poli, Pierre-Marie Poulain, Gilles Reverdin, Xiujun Sun, Val Swail, Sidney Thurston, Lixin Wu, Lisan Yu, Bin Wang, and Dongxiao Zhang
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global in situ observations ,air-sea interface ,essential climate and ocean variables ,climate variability and change ,weather forecasting ,SVP drifters ,Science ,General. Including nature conservation, geographical distribution ,QH1-199.5 - Abstract
The air–sea interface is a key gateway in the Earth system. It is where the atmosphere sets the ocean in motion, climate/weather-relevant air–sea processes occur, and pollutants (i.e., plastic, anthropogenic carbon dioxide, radioactive/chemical waste) enter the sea. Hence, accurate estimates and forecasts of physical and biogeochemical processes at this interface are critical for sustainable blue economy planning, growth, and disaster mitigation. Such estimates and forecasts rely on accurate and integrated in situ and satellite surface observations. High-impact uses of ocean surface observations of essential ocean/climate variables (EOVs/ECVs) include (1) assimilation into/validation of weather, ocean, and climate forecast models to improve their skill, impact, and value; (2) ocean physics studies (i.e., heat, momentum, freshwater, and biogeochemical air–sea fluxes) to further our understanding and parameterization of air–sea processes; and (3) calibration and validation of satellite ocean products (i.e., currents, temperature, salinity, sea level, ocean color, wind, and waves). We review strengths and limitations, impacts, and sustainability of in situ ocean surface observations of several ECVs and EOVs. We draw a 10-year vision of the global ocean surface observing network for improved synergy and integration with other observing systems (e.g., satellites), for modeling/forecast efforts, and for a better ocean observing governance. The context is both the applications listed above and the guidelines of frameworks such as the Global Ocean Observing System (GOOS) and Global Climate Observing System (GCOS) (both co-sponsored by the Intergovernmental Oceanographic Commission of UNESCO, IOC–UNESCO; the World Meteorological Organization, WMO; the United Nations Environment Programme, UNEP; and the International Science Council, ISC). Networks of multiparametric platforms, such as the global drifter array, offer opportunities for new and improved in situ observations. Advances in sensor technology (e.g., low-cost wave sensors), high-throughput communications, evolving cyberinfrastructures, and data information systems with potential to improve the scope, efficiency, integration, and sustainability of the ocean surface observing system are explored.
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- 2019
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21. Air-Sea Fluxes With a Focus on Heat and Momentum
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Meghan F. Cronin, Chelle L. Gentemann, James Edson, Iwao Ueki, Mark Bourassa, Shannon Brown, Carol Anne Clayson, Chris W. Fairall, J. Thomas Farrar, Sarah T. Gille, Sergey Gulev, Simon A. Josey, Seiji Kato, Masaki Katsumata, Elizabeth Kent, Marjolaine Krug, Peter J. Minnett, Rhys Parfitt, Rachel T. Pinker, Paul W. Stackhouse, Sebastiaan Swart, Hiroyuki Tomita, Douglas Vandemark, A. Robert Weller, Kunio Yoneyama, Lisan Yu, and Dongxiao Zhang
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air-sea heat flux ,latent heat flux ,surface radiation ,ocean wind stress ,autonomous surface vehicle ,OceanSITES ,Science ,General. Including nature conservation, geographical distribution ,QH1-199.5 - Abstract
Turbulent and radiative exchanges of heat between the ocean and atmosphere (hereafter heat fluxes), ocean surface wind stress, and state variables used to estimate them, are Essential Ocean Variables (EOVs) and Essential Climate Variables (ECVs) influencing weather and climate. This paper describes an observational strategy for producing 3-hourly, 25-km (and an aspirational goal of hourly at 10-km) heat flux and wind stress fields over the global, ice-free ocean with breakthrough 1-day random uncertainty of 15 W m–2 and a bias of less than 5 W m–2. At present this accuracy target is met only for OceanSITES reference station moorings and research vessels (RVs) that follow best practices. To meet these targets globally, in the next decade, satellite-based observations must be optimized for boundary layer measurements of air temperature, humidity, sea surface temperature, and ocean wind stress. In order to tune and validate these satellite measurements, a complementary global in situ flux array, built around an expanded OceanSITES network of time series reference station moorings, is also needed. The array would include 500–1000 measurement platforms, including autonomous surface vehicles, moored and drifting buoys, RVs, the existing OceanSITES network of 22 flux sites, and new OceanSITES expanded in 19 key regions. This array would be globally distributed, with 1–3 measurement platforms in each nominal 10° by 10° box. These improved moisture and temperature profiles and surface data, if assimilated into Numerical Weather Prediction (NWP) models, would lead to better representation of cloud formation processes, improving state variables and surface radiative and turbulent fluxes from these models. The in situ flux array provides globally distributed measurements and metrics for satellite algorithm development, product validation, and for improving satellite-based, NWP and blended flux products. In addition, some of these flux platforms will also measure direct turbulent fluxes, which can be used to improve algorithms for computation of air-sea exchange of heat and momentum in flux products and models. With these improved air-sea fluxes, the ocean’s influence on the atmosphere will be better quantified and lead to improved long-term weather forecasts, seasonal-interannual-decadal climate predictions, and regional climate projections.
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- 2019
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22. Skin Sea-Surface Temperature from VIIRS on Suomi-NPP—NASA Continuity Retrievals
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Peter J. Minnett, Katherine A. Kilpatrick, Guillermo P. Podestá, Robert H. Evans, Malgorzata D. Szczodrak, Miguel Angel Izaguirre, Elizabeth J. Williams, Susan Walsh, R. Michael Reynolds, Sean W. Bailey, Edward M. Armstrong, and Jorge Vazquez-Cuervo
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sea-surface temperature ,SST ,VIIRS ,Visible Infrared Imaging Radiometer Suite ,Suomi-NPP ,Science - Abstract
Retrievals of skin Sea-Surface Temperature (SSTskin) from the measurements of the Visible Infrared Imaging Radiometer Suite on the Suomi-National Polar-orbiting Partnership satellite are presented and discussed. The algorithms used to derive the SSTskin from the radiometric measurements are given in detail. A number of approaches to assess the accuracy and stability of the Visible Infrared Imaging Radiometer Suite (VIIRS) SSTskin retrievals are reported, and factors including latitude and season, and physical processes in the atmosphere and at the surface are discussed. We conclude that the Suomi National Polar-orbiting Partnership (S-NPP) VIIRS is capable of matching and improving upon the accuracies of SSTskin from the MODISs on Terra and Aqua, and that the VIIRS SSTskin fields have the potential to contribute to the extension of the satellite-derived Climate Data Records of SST into the future.
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- 2020
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23. Comparison of SLSTR Thermal Emissive Bands Clear-Sky Measurements with Those of Geostationary Imagers
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Bingkun Luo and Peter J. Minnett
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SLSTR ,evaluation ,thermal bands ,ABI ,SEVIRI ,Science - Abstract
The Sentinel-3 series satellites belong to the European Earth Observation satellite missions for supporting oceanography, land, and atmospheric studies. The Sea and Land Surface Temperature Radiometer (SLSTR) onboard the Sentinel-3 satellites was designed to provide a significant improvement in remote sensing of skin sea surface temperature (SSTskin). The successful application of SLSTR-derived SSTskin fields depends on their accuracies. Based on sensor-dependent radiative transfer model simulations, geostationary Geostationary Operational Environmental Satellite (GOES-16) Advanced Baseline Imagers (ABI) and Meteosat Second Generation (MSG-4) Spinning Enhanced Visible and Infrared Imager (SEVIRI) brightness temperatures (BT) have been transformed to SLSTR equivalents to permit comparisons at the pixel level in three ocean regions. The results show the averaged BT differences are on the order of 0.1 K and the existence of small biases between them are likely due to the uncertainties in cloud masking, satellite view angle, solar azimuth angle, and reflected solar light. This study demonstrates the feasibility of combining SSTskin retrievals from SLSTR with those of ABI and SEVIRI.
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- 2020
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24. Evaluation of the ERA5 Sea Surface Skin Temperature with Remotely-Sensed Shipborne Marine-Atmospheric Emitted Radiance Interferometer Data
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Bingkun Luo and Peter J. Minnett
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ERA5 ,evaluation ,sea surface skin temperature ,M-AERI ,Science - Abstract
Sea surface temperature is very important in weather and ocean forecasting, and studying the ocean, atmosphere and climate system. Measuring the sea surface skin temperature (SSTskin) with infrared radiometers onboard earth observation satellites and shipboard instruments is a mature subject spanning several decades. Reanalysis model output SSTskin, such as from the newly released ERA5, is very widely used and has been applied for monitoring climate change, weather prediction research, and other commercial applications. The ERA5 output SSTskin data must be rigorously evaluated to meet the stringent accuracy requirements for climate research. This study aims to estimate the accuracy of the ERA5 SSTskin fields and provide an associated error estimate by using measurements from accurate shipboard infrared radiometers: the Marine-Atmosphere Emitted Radiance Interferometers (M-AERIs). Overall, the ERA5 SSTskin has high correlation with ship-based radiometric measurements, with an average difference of~0.2 K with a Pearson correlation coefficient (R) of 0.993. Parts of the discrepancies are related to dust aerosols and variability in air-sea temperature differences. The downward radiative flux due to dust aerosols leads to significant SSTskin differences for ERA5. The SSTskin differences are greater with the large, positive air–sea temperature differences. This study provides suggestions for the applicability of ERA5 SSTskin fields in a selection of research applications.
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- 2020
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25. FluxSat: Measuring the Ocean–Atmosphere Turbulent Exchange of Heat and Moisture from Space
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Chelle L. Gentemann, Carol Anne Clayson, Shannon Brown, Tong Lee, Rhys Parfitt, J. Thomas Farrar, Mark Bourassa, Peter J. Minnett, Hyodae Seo, Sarah T. Gille, and Victor Zlotnicki
- Subjects
air–sea interactions ,mesoscale ,fluxes ,Science - Abstract
Recent results using wind and sea surface temperature data from satellites and high-resolution coupled models suggest that mesoscale ocean–atmosphere interactions affect the locations and evolution of storms and seasonal precipitation over continental regions such as the western US and Europe. The processes responsible for this coupling are difficult to verify due to the paucity of accurate air–sea turbulent heat and moisture flux data. These fluxes are currently derived by combining satellite measurements that are not coincident and have differing and relatively low spatial resolutions, introducing sampling errors that are largest in regions with high spatial and temporal variability. Observational errors related to sensor design also contribute to increased uncertainty. Leveraging recent advances in sensor technology, we here describe a satellite mission concept, FluxSat, that aims to simultaneously measure all variables necessary for accurate estimation of ocean–atmosphere turbulent heat and moisture fluxes and capture the effect of oceanic mesoscale forcing. Sensor design is expected to reduce observational errors of the latent and sensible heat fluxes by almost 50%. FluxSat will improve the accuracy of the fluxes at spatial scales critical to understanding the coupled ocean–atmosphere boundary layer system, providing measurements needed to improve weather forecasts and climate model simulations.
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- 2020
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26. Classification of Oil Slicks and Look-Alike Slicks: A Linear Discriminant Analysis of Microwave, Infrared, and Optical Satellite Measurements
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Gustavo de Araújo Carvalho, Peter J. Minnett, Nelson F. F. Ebecken, and Luiz Landau
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Linear Discriminant Analysis (LDA) ,satellite image classification and segmentation algorithm ,microwave radar ,infrared sensor ,optical remote sensing ,wind scatterometer ,Science - Abstract
We classify low-backscatter regions observed in Synthetic Aperture Radar (SAR) measurements of the surface of the ocean as either oil slicks or look-alike slicks (radar false targets). Our proposed classification algorithm is based on Linear Discriminant Analyses (LDAs) of RADARSAT-1 measurements (402 scenes off the southeast coast of Brazil from July 2001 to June 2003) and Meteorological-Oceanographic (MetOc) data from other earth observation sensors: Advanced Very High Resolution Radiometer (AVHRR), Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Quick Scatterometer (QuikSCAT). Oil slicks are sea-surface expressions of exploration and production oil, ship- and orphan-spills. False targets are associated with environmental phenomena, such as biogenic films, algal blooms, upwelling, low wind, or rain cells. Both categories have been interpreted by domain-experts: mineral oil (n = 350; 45.5%) and petroleum free (n = 419; 54.5%). We explore nine size variables (area, perimeter, etc.) and three types of MetOc information (sea surface temperature, chlorophyll-a, and wind speed) that describe the 769 samples analyzed. Seven attribute–domain combinations are tested with three non-linear transformations (none, cube root, log10), with and without MetOc, adding to 39 attribute subdivisions. Classification accuracies are independent of data transformation and improve when selected size attributes are combined with MetOc, leading to overall accuracies of ~80% and sound levels of sensitivity (~90%), specificity (~80%), positive (~80%) and negative (~90%) predictive values. The effectiveness of this data-driven attempt supports further commercial or academic implementation of our LDA algorithm.
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- 2020
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27. EUREC4A
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Stevens, B, Bony, S, Farrell, D, Ament, F, Blyth, A, Fairall, C, Karstensen, J, Quinn, P, Speich, S, Acquistapace, C, Aemisegger, F, Albright, A, Bellenger, H, Bodenschatz, E, Caesar, K, Chewitt-Lucas, R, De Boer, G, Delanoe, J, Denby, L, Ewald, F, Fildier, B, Forde, M, George, G, Gross, S, Hagen, M, Hausold, A, Heywood, K, Hirsch, L, Jacob, M, Jansen, F, Kinne, S, Klocke, D, Kolling, T, Konow, H, Lothon, M, Mohr, W, Naumann, A, Nuijens, L, Olivier, L, Pincus, R, Pohlker, M, Reverdin, G, Roberts, G, Schnitt, S, Schulz, H, Pier Siebesma, A, Stephan, C, Sullivan, P, Touze-Peiffer, L, Vial, J, Vogel, R, Zuidema, P, Alexander, N, Alves, L, Arixi, S, Asmath, H, Bagheri, G, Baier, K, Bailey, A, Baranowski, D, Baron, A, Barrau, S, Barrett, P, Batier, F, Behrendt, A, Bendinger, A, Beucher, F, Bigorre, S, Blades, E, Blossey, P, Bock, O, Boing, S, Bosser, P, Bourras, D, Bouruet-Aubertot, P, Bower, K, Branellec, P, Branger, H, Brennek, M, Brewer, A, Brilouet, P, Brugmann, B, Buehler, S, Burke, E, Burton, R, Calmer, R, Canonici, J, Carton, X, Cato, G, Charles, J, Chazette, P, Chen, Y, Chilinski, M, Choularton, T, Chuang, P, Clarke, S, Coe, H, Cornet, C, Coutris, P, Couvreux, F, Crewell, S, Cronin, T, Cui, Z, Cuypers, Y, Daley, A, Damerell, G, Dauhut, T, Deneke, H, Desbios, J, Dorner, S, Donner, S, Douet, V, Drushka, K, Dutsch, M, Ehrlich, A, Emanuel, K, Emmanouilidis, A, Etienne, J, Etienne-Leblanc, S, Faure, G, Feingold, G, Ferrero, L, Fix, A, Flamant, C, Flatau, P, Foltz, G, Forster, L, Furtuna, I, Gadian, A, Galewsky, J, Gallagher, M, Gallimore, P, Gaston, C, Gentemann, C, Geyskens, N, Giez, A, Gollop, J, Gouirand, I, Gourbeyre, C, De Graaf, D, De Groot, G, Grosz, R, Guttler, J, Gutleben, M, Hall, K, Harris, G, Helfer, K, Henze, D, Herbert, C, Holanda, B, Ibanez-Landeta, A, Intrieri, J, Iyer, S, Julien, F, Kalesse, H, Kazil, J, Kellman, A, Kidane, A, Kirchner, U, Klingebiel, M, Korner, M, Kremper, L, Kretzschmar, J, Kruger, O, Kumala, W, Kurz, A, L'Hegaret, P, Labaste, M, Lachlan-Cope, T, Laing, A, Landschutzer, P, Lang, T, Lange, D, Lange, I, Laplace, C, Lavik, G, Laxenaire, R, Lebihan, C, Leandro, M, Lefevre, N, Lena, M, Lenschow, D, Li, Q, Lloyd, G, Los, S, Losi, N, Lovell, O, Luneau, C, Makuch, P, Malinowski, S, Manta, G, Marinou, E, Marsden, N, Masson, S, Maury, N, Mayer, B, Mayers-Als, M, Mazel, C, Mcgeary, W, Mcwilliams, J, Mech, M, Mehlmann, M, Meroni, A, Mieslinger, T, Minikin, A, Minnett, P, Moller, G, Avalos, Y, Muller, C, Musat, I, Napoli, A, Neuberger, A, Noisel, C, Noone, D, Nordsiek, F, Nowak, J, Oswald, L, Parker, D, Peck, C, Person, R, Philippi, M, Plueddemann, A, Pohlker, C, Portge, V, Poschl, U, Pologne, L, Posyniak, M, Prange, M, Melendez, E, Radtke, J, Ramage, K, Reimann, J, Renault, L, Reus, K, Reyes, A, Ribbe, J, Ringel, M, Ritschel, M, Rocha, C, Rochetin, N, Rottenbacher, J, Rollo, C, Royer, H, Sadoulet, P, Saffin, L, Sandiford, S, Sandu, I, Schafer, M, Schemann, V, Schirmacher, I, Schlenczek, O, Schmidt, J, Schroder, M, Schwarzenboeck, A, Sealy, A, Senff, C, Serikov, I, Shohan, S, Siddle, E, Smirnov, A, Spath, F, Spooner, B, Katharina Stolla, M, Szkolka, W, De Szoeke, S, Tarot, S, Tetoni, E, Thompson, E, Thomson, J, Tomassini, L, Totems, J, Ubele, A, Villiger, L, Von Arx, J, Wagner, T, Walther, A, Webber, B, Wendisch, M, Whitehall, S, Wiltshire, A, Wing, A, Wirth, M, Wiskandt, J, Wolf, K, Worbes, L, Wright, E, Wulfmeyer, V, Young, S, Zhang, C, Zhang, D, Ziemen, F, Zinner, T, Zoger, M, Stevens B., Bony S., Farrell D., Ament F., Blyth A., Fairall C., Karstensen J., Quinn P. K., Speich S., Acquistapace C., Aemisegger F., Albright A. L., Bellenger H., Bodenschatz E., Caesar K. -A., Chewitt-Lucas R., De Boer G., Delanoe J., Denby L., Ewald F., Fildier B., Forde M., George G., Gross S., Hagen M., Hausold A., Heywood K. J., Hirsch L., Jacob M., Jansen F., Kinne S., Klocke D., Kolling T., Konow H., Lothon M., Mohr W., Naumann A. K., Nuijens L., Olivier L., Pincus R., Pohlker M., Reverdin G., Roberts G., Schnitt S., Schulz H., Pier Siebesma A., Stephan C. C., Sullivan P., Touze-Peiffer L., Vial J., Vogel R., Zuidema P., Alexander N., Alves L., Arixi S., Asmath H., Bagheri G., Baier K., Bailey A., Baranowski D., Baron A., Barrau S., Barrett P. A., Batier F., Behrendt A., Bendinger A., Beucher F., Bigorre S., Blades E., Blossey P., Bock O., Boing S., Bosser P., Bourras D., Bouruet-Aubertot P., Bower K., Branellec P., Branger H., Brennek M., Brewer A., Brilouet P. -E., Brugmann B., Buehler S. A., Burke E., Burton R., Calmer R., Canonici J. -C., Carton X., Cato G., Charles J. A., Chazette P., Chen Y., Chilinski M. T., Choularton T., Chuang P., Clarke S., Coe H., Cornet C., Coutris P., Couvreux F., Crewell S., Cronin T., Cui Z., Cuypers Y., Daley A., Damerell G. M., Dauhut T., Deneke H., Desbios J. -P., Dorner S., Donner S., Douet V., Drushka K., Dutsch M., Ehrlich A., Emanuel K., Emmanouilidis A., Etienne J. -C., Etienne-Leblanc S., Faure G., Feingold G., Ferrero L., Fix A., Flamant C., Flatau P. J., Foltz G. R., Forster L., Furtuna I., Gadian A., Galewsky J., Gallagher M., Gallimore P., Gaston C., Gentemann C., Geyskens N., Giez A., Gollop J., Gouirand I., Gourbeyre C., De Graaf D., De Groot G. E., Grosz R., Guttler J., Gutleben M., Hall K., Harris G., Helfer K. C., Henze D., Herbert C., Holanda B., Ibanez-Landeta A., Intrieri J., Iyer S., Julien F., Kalesse H., Kazil J., Kellman A., Kidane A. T., Kirchner U., Klingebiel M., Korner M., Kremper L. A., Kretzschmar J., Kruger O., Kumala W., Kurz A., L'Hegaret P., Labaste M., Lachlan-Cope T., Laing A., Landschutzer P., Lang T., Lange D., Lange I., Laplace C., Lavik G., Laxenaire R., LeBihan C., Leandro M., Lefevre N., Lena M., Lenschow D., Li Q., Lloyd G., Los S., Losi N., Lovell O., Luneau C., Makuch P., Malinowski S., Manta G., Marinou E., Marsden N., Masson S., Maury N., Mayer B., Mayers-Als M., Mazel C., McGeary W., McWilliams J. C., Mech M., Mehlmann M., Meroni A. N., Mieslinger T., Minikin A., Minnett P., Moller G., Avalos Y. M., Muller C., Musat I., Napoli A., Neuberger A., Noisel C., Noone D., Nordsiek F., Nowak J. L., Oswald L., Parker D. J., Peck C., Person R., Philippi M., Plueddemann A., Pohlker C., Portge V., Poschl U., Pologne L., Posyniak M., Prange M., Melendez E. Q., Radtke J., Ramage K., Reimann J., Renault L., Reus K., Reyes A., Ribbe J., Ringel M., Ritschel M., Rocha C. B., Rochetin N., Rottenbacher J., Rollo C., Royer H., Sadoulet P., Saffin L., Sandiford S., Sandu I., Schafer M., Schemann V., Schirmacher I., Schlenczek O., Schmidt J., Schroder M., Schwarzenboeck A., Sealy A., Senff C. J., Serikov I., Shohan S., Siddle E., Smirnov A., Spath F., Spooner B., Katharina Stolla M., Szkolka W., De Szoeke S. P., Tarot S., Tetoni E., Thompson E., Thomson J., Tomassini L., Totems J., Ubele A. A., Villiger L., Von Arx J., Wagner T., Walther A., Webber B., Wendisch M., Whitehall S., Wiltshire A., Wing A. A., Wirth M., Wiskandt J., Wolf K., Worbes L., Wright E., Wulfmeyer V., Young S., Zhang C., Zhang D., Ziemen F., Zinner T., Zoger M., Stevens, B, Bony, S, Farrell, D, Ament, F, Blyth, A, Fairall, C, Karstensen, J, Quinn, P, Speich, S, Acquistapace, C, Aemisegger, F, Albright, A, Bellenger, H, Bodenschatz, E, Caesar, K, Chewitt-Lucas, R, De Boer, G, Delanoe, J, Denby, L, Ewald, F, Fildier, B, Forde, M, George, G, Gross, S, Hagen, M, Hausold, A, Heywood, K, Hirsch, L, Jacob, M, Jansen, F, Kinne, S, Klocke, D, Kolling, T, Konow, H, Lothon, M, Mohr, W, Naumann, A, Nuijens, L, Olivier, L, Pincus, R, Pohlker, M, Reverdin, G, Roberts, G, Schnitt, S, Schulz, H, Pier Siebesma, A, Stephan, C, Sullivan, P, Touze-Peiffer, L, Vial, J, Vogel, R, Zuidema, P, Alexander, N, Alves, L, Arixi, S, Asmath, H, Bagheri, G, Baier, K, Bailey, A, Baranowski, D, Baron, A, Barrau, S, Barrett, P, Batier, F, Behrendt, A, Bendinger, A, Beucher, F, Bigorre, S, Blades, E, Blossey, P, Bock, O, Boing, S, Bosser, P, Bourras, D, Bouruet-Aubertot, P, Bower, K, Branellec, P, Branger, H, Brennek, M, Brewer, A, Brilouet, P, Brugmann, B, Buehler, S, Burke, E, Burton, R, Calmer, R, Canonici, J, Carton, X, Cato, G, Charles, J, Chazette, P, Chen, Y, Chilinski, M, Choularton, T, Chuang, P, Clarke, S, Coe, H, Cornet, C, Coutris, P, Couvreux, F, Crewell, S, Cronin, T, Cui, Z, Cuypers, Y, Daley, A, Damerell, G, Dauhut, T, Deneke, H, Desbios, J, Dorner, S, Donner, S, Douet, V, Drushka, K, Dutsch, M, Ehrlich, A, Emanuel, K, Emmanouilidis, A, Etienne, J, Etienne-Leblanc, S, Faure, G, Feingold, G, Ferrero, L, Fix, A, Flamant, C, Flatau, P, Foltz, G, Forster, L, Furtuna, I, Gadian, A, Galewsky, J, Gallagher, M, Gallimore, P, Gaston, C, Gentemann, C, Geyskens, N, Giez, A, Gollop, J, Gouirand, I, Gourbeyre, C, De Graaf, D, De Groot, G, Grosz, R, Guttler, J, Gutleben, M, Hall, K, Harris, G, Helfer, K, Henze, D, Herbert, C, Holanda, B, Ibanez-Landeta, A, Intrieri, J, Iyer, S, Julien, F, Kalesse, H, Kazil, J, Kellman, A, Kidane, A, Kirchner, U, Klingebiel, M, Korner, M, Kremper, L, Kretzschmar, J, Kruger, O, Kumala, W, Kurz, A, L'Hegaret, P, Labaste, M, Lachlan-Cope, T, Laing, A, Landschutzer, P, Lang, T, Lange, D, Lange, I, Laplace, C, Lavik, G, Laxenaire, R, Lebihan, C, Leandro, M, Lefevre, N, Lena, M, Lenschow, D, Li, Q, Lloyd, G, Los, S, Losi, N, Lovell, O, Luneau, C, Makuch, P, Malinowski, S, Manta, G, Marinou, E, Marsden, N, Masson, S, Maury, N, Mayer, B, Mayers-Als, M, Mazel, C, Mcgeary, W, Mcwilliams, J, Mech, M, Mehlmann, M, Meroni, A, Mieslinger, T, Minikin, A, Minnett, P, Moller, G, Avalos, Y, Muller, C, Musat, I, Napoli, A, Neuberger, A, Noisel, C, Noone, D, Nordsiek, F, Nowak, J, Oswald, L, Parker, D, Peck, C, Person, R, Philippi, M, Plueddemann, A, Pohlker, C, Portge, V, Poschl, U, Pologne, L, Posyniak, M, Prange, M, Melendez, E, Radtke, J, Ramage, K, Reimann, J, Renault, L, Reus, K, Reyes, A, Ribbe, J, Ringel, M, Ritschel, M, Rocha, C, Rochetin, N, Rottenbacher, J, Rollo, C, Royer, H, Sadoulet, P, Saffin, L, Sandiford, S, Sandu, I, Schafer, M, Schemann, V, Schirmacher, I, Schlenczek, O, Schmidt, J, Schroder, M, Schwarzenboeck, A, Sealy, A, Senff, C, Serikov, I, Shohan, S, Siddle, E, Smirnov, A, Spath, F, Spooner, B, Katharina Stolla, M, Szkolka, W, De Szoeke, S, Tarot, S, Tetoni, E, Thompson, E, Thomson, J, Tomassini, L, Totems, J, Ubele, A, Villiger, L, Von Arx, J, Wagner, T, Walther, A, Webber, B, Wendisch, M, Whitehall, S, Wiltshire, A, Wing, A, Wirth, M, Wiskandt, J, Wolf, K, Worbes, L, Wright, E, Wulfmeyer, V, Young, S, Zhang, C, Zhang, D, Ziemen, F, Zinner, T, Zoger, M, Stevens B., Bony S., Farrell D., Ament F., Blyth A., Fairall C., Karstensen J., Quinn P. K., Speich S., Acquistapace C., Aemisegger F., Albright A. L., Bellenger H., Bodenschatz E., Caesar K. -A., Chewitt-Lucas R., De Boer G., Delanoe J., Denby L., Ewald F., Fildier B., Forde M., George G., Gross S., Hagen M., Hausold A., Heywood K. J., Hirsch L., Jacob M., Jansen F., Kinne S., Klocke D., Kolling T., Konow H., Lothon M., Mohr W., Naumann A. K., Nuijens L., Olivier L., Pincus R., Pohlker M., Reverdin G., Roberts G., Schnitt S., Schulz H., Pier Siebesma A., Stephan C. C., Sullivan P., Touze-Peiffer L., Vial J., Vogel R., Zuidema P., Alexander N., Alves L., Arixi S., Asmath H., Bagheri G., Baier K., Bailey A., Baranowski D., Baron A., Barrau S., Barrett P. A., Batier F., Behrendt A., Bendinger A., Beucher F., Bigorre S., Blades E., Blossey P., Bock O., Boing S., Bosser P., Bourras D., Bouruet-Aubertot P., Bower K., Branellec P., Branger H., Brennek M., Brewer A., Brilouet P. -E., Brugmann B., Buehler S. A., Burke E., Burton R., Calmer R., Canonici J. -C., Carton X., Cato G., Charles J. A., Chazette P., Chen Y., Chilinski M. T., Choularton T., Chuang P., Clarke S., Coe H., Cornet C., Coutris P., Couvreux F., Crewell S., Cronin T., Cui Z., Cuypers Y., Daley A., Damerell G. M., Dauhut T., Deneke H., Desbios J. -P., Dorner S., Donner S., Douet V., Drushka K., Dutsch M., Ehrlich A., Emanuel K., Emmanouilidis A., Etienne J. -C., Etienne-Leblanc S., Faure G., Feingold G., Ferrero L., Fix A., Flamant C., Flatau P. J., Foltz G. R., Forster L., Furtuna I., Gadian A., Galewsky J., Gallagher M., Gallimore P., Gaston C., Gentemann C., Geyskens N., Giez A., Gollop J., Gouirand I., Gourbeyre C., De Graaf D., De Groot G. E., Grosz R., Guttler J., Gutleben M., Hall K., Harris G., Helfer K. C., Henze D., Herbert C., Holanda B., Ibanez-Landeta A., Intrieri J., Iyer S., Julien F., Kalesse H., Kazil J., Kellman A., Kidane A. T., Kirchner U., Klingebiel M., Korner M., Kremper L. A., Kretzschmar J., Kruger O., Kumala W., Kurz A., L'Hegaret P., Labaste M., Lachlan-Cope T., Laing A., Landschutzer P., Lang T., Lange D., Lange I., Laplace C., Lavik G., Laxenaire R., LeBihan C., Leandro M., Lefevre N., Lena M., Lenschow D., Li Q., Lloyd G., Los S., Losi N., Lovell O., Luneau C., Makuch P., Malinowski S., Manta G., Marinou E., Marsden N., Masson S., Maury N., Mayer B., Mayers-Als M., Mazel C., McGeary W., McWilliams J. C., Mech M., Mehlmann M., Meroni A. N., Mieslinger T., Minikin A., Minnett P., Moller G., Avalos Y. M., Muller C., Musat I., Napoli A., Neuberger A., Noisel C., Noone D., Nordsiek F., Nowak J. L., Oswald L., Parker D. J., Peck C., Person R., Philippi M., Plueddemann A., Pohlker C., Portge V., Poschl U., Pologne L., Posyniak M., Prange M., Melendez E. Q., Radtke J., Ramage K., Reimann J., Renault L., Reus K., Reyes A., Ribbe J., Ringel M., Ritschel M., Rocha C. B., Rochetin N., Rottenbacher J., Rollo C., Royer H., Sadoulet P., Saffin L., Sandiford S., Sandu I., Schafer M., Schemann V., Schirmacher I., Schlenczek O., Schmidt J., Schroder M., Schwarzenboeck A., Sealy A., Senff C. J., Serikov I., Shohan S., Siddle E., Smirnov A., Spath F., Spooner B., Katharina Stolla M., Szkolka W., De Szoeke S. P., Tarot S., Tetoni E., Thompson E., Thomson J., Tomassini L., Totems J., Ubele A. A., Villiger L., Von Arx J., Wagner T., Walther A., Webber B., Wendisch M., Whitehall S., Wiltshire A., Wing A. A., Wirth M., Wiskandt J., Wolf K., Worbes L., Wright E., Wulfmeyer V., Young S., Zhang C., Zhang D., Ziemen F., Zinner T., and Zoger M.
- Abstract
The science guiding the EUREC4A campaign and its measurements is presented. EUREC4A comprised roughly 5 weeks of measurements in the downstream winter trades of the North Atlantic - eastward and southeastward of Barbados. Through its ability to characterize processes operating across a wide range of scales, EUREC4A marked a turning point in our ability to observationally study factors influencing clouds in the trades, how they will respond to warming, and their link to other components of the earth system, such as upper-ocean processes or the life cycle of particulate matter. This characterization was made possible by thousands (2500) of sondes distributed to measure circulations on meso- (200 km) and larger (500 km) scales, roughly 400 h of flight time by four heavily instrumented research aircraft; four global-class research vessels; an advanced ground-based cloud observatory; scores of autonomous observing platforms operating in the upper ocean (nearly 10 000 profiles), lower atmosphere (continuous profiling), and along the air-sea interface; a network of water stable isotopologue measurements; targeted tasking of satellite remote sensing; and modeling with a new generation of weather and climate models. In addition to providing an outline of the novel measurements and their composition into a unified and coordinated campaign, the six distinct scientific facets that EUREC4A explored - from North Brazil Current rings to turbulence-induced clustering of cloud droplets and its influence on warm-rain formation - are presented along with an overview of EUREC4A's outreach activities, environmental impact, and guidelines for scientific practice. Track data for all platforms are standardized and accessible at 10.25326/165 , and a film documenting the campaign is provided as a video supplement.
- Published
- 2021
28. Oil-Slick Category Discrimination (Seeps vs. Spills): A Linear Discriminant Analysis Using RADARSAT-2 Backscatter Coefficients (σ°, β°, and γ°) in Campeche Bay (Gulf of Mexico)
- Author
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Gustavo de Araújo Carvalho, Peter J. Minnett, Eduardo T. Paes, Fernando P. de Miranda, and Luiz Landau
- Subjects
ocean remote sensing ,satellite image classification and segmentation ,RADARSAT ,synthetic aperture radar (SAR) ,linear discriminant analysis (LDA) ,physical oceanography ,oil slicks ,oil spills ,oil seeps ,Campeche Bay (Gulf of Mexico) ,Science - Abstract
A novel empirical approach to categorize oil slicks’ sea surface expressions in synthetic aperture radar (SAR) measurements into oil seeps or oil spills is investigated, contributing both to academic remote sensing research and to practical applications for the petroleum industry. We use linear discriminant analysis (LDA) to try accuracy improvements from our previously published methods of discriminating seeps from spills that achieved ~70% of overall accuracy. Analyzing 244 RADARSAT-2 scenes containing 4562 slicks observed in Campeche Bay (Gulf of Mexico), our exploratory data analysis evaluates the impact of 61 combinations of SAR backscatter coefficients (σ°, β°, γ°), SAR calibrated products (received radar beam given in amplitude or decibel, with or without a despeckle filter), and data transformations (none, cube root, log10). The LDA ability to discriminate the oil-slick category is rather independent of backscatter coefficients and calibrated products, but influenced by data transformations. The combination of attributes plays a role in the discrimination; combining oil-slicks’ size and SAR information is more effective. We have simplified our analyses using fewer attributes to reach accuracies comparable to those of our earlier studies, and we suggest using other multivariate data analyses—cubist or random forest—to attempt to further improve oil-slick category discrimination.
- Published
- 2019
- Full Text
- View/download PDF
29. Tools of Engagement: Sharing Evidence of Student Engagement Sparks Changes in Teacher Practice
- Author
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Minnett, Ann, Murphy, Mike, Nobles, Sandy, and Taylor, Trina
- Abstract
When visitors tour the authors' classrooms at the J. Erik Jonsson Community School, a 3-year-old through 5th-grade laboratory school just south of downtown Dallas, Texas, they sense that something is different. Visitors remark about the respectful, caring environment of the school and the high-powered instruction, and they want to learn how they can implement these qualities in their own schools. As part of the research, professional learning, and leadership team at the Jonsson School, the authors regularly share the work of the Jonsson School with other educators and communicate Jonsson's simple success formula: Powerful pedagogy + trusting relationships = student engagement for learning. In fall 2006, they grew curious about what the teachers were actually doing in the classroom to elicit this powerful student engagement. They hypothesized that if they could develop a way to collect evidence about student engagement in classrooms and share that evidence with their teachers, they would begin to transform their practices based on what they were learning about their students. The authors' supposition was supported by NSDC's Standards for Staff Development Data-Driven standard, which reminds them that "the study of such [classroom] evidence is itself a potent means of staff development" (NSDC, 2001). The authors asked many questions. Their questions became the classroom research during the school year 2006-07, and the data and dialogue with participating teachers created a startling exchange of evidence and resulted in changes in teacher practices.
- Published
- 2008
30. CIRENE : Air–Sea Interactions in the Seychelles–Chagos Thermocline Ridge Region
- Author
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Vialard, J., Duvel, J. P., McPhaden, M. J., Bouruet-Aubertot, P., Ward, B., Key, E., Bourras, D., Weller, R., Minnett, P., Weill, A., Cassou, C., Eymard, L., Fristedt, T., Basdevant, C., Dandonneau, Y., Duteil, O., Izumo, T., de Boyer Montégut, C., Masson, S., Marsac, F., Menkes, C., and Kennan, S.
- Published
- 2009
31. SUPPLEMENT : VASCO–CIRENE MEASUREMENTS
- Author
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Vialard, J., Duvel, J. P., McPhaden, M. J., Bouruet-Aubertot, P., Ward, B., Key, E., Bourras, D., Weller, R., Minnett, P., Weill, A., Cassou, C., Eymard, L., Fristedt, T., Basdevant, C., Dandonneau, Y., Duteil, O., Izumo, T., de Boyer Montégut, C., Masson, S., Marsac, F., Menkes, C., and Kennan, S.
- Published
- 2009
32. Refined Analysis of RADARSAT-2 Measurements to Discriminate Two Petrogenic Oil-Slick Categories: Seeps versus Spills
- Author
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Gustavo de Araújo Carvalho, Peter J. Minnett, Eduardo Tavares Paes, Fernando Pellon de Miranda, and Luiz Landau
- Subjects
oil-slick discrimination algorithm ,petrogenic oil-slick category ,naturally-occurring oil seeps ,man-made oil spills ,exploratory data analysis ,remote sensing ,synthetic aperture radar ,RADARSAT ,Gulf of Mexico ,Campeche Bay ,Naval architecture. Shipbuilding. Marine engineering ,VM1-989 ,Oceanography ,GC1-1581 - Abstract
Our research focuses on refining the ability to discriminate two petrogenic oil-slick categories: the sea surface expression of naturally-occurring oil seeps and man-made oil spills. For that, a long-term RADARSAT-2 dataset (244 scenes imaged between 2008 and 2012) is analyzed to investigate oil slicks (4562) observed in the Gulf of Mexico (Campeche Bay, Mexico). As the scientific literature on the use of satellite-derived measurements to discriminate the oil-slick category is sparse, our research addresses this gap by extending our previous investigations aimed at discriminating seeps from spills. To reveal hidden traits of the available satellite information and to evaluate an existing Oil-Slick Discrimination Algorithm, distinct processing segments methodically inspect the data at several levels: input data repository, data transformation, attribute selection, and multivariate data analysis. Different attribute selection strategies similarly excel at the seep-spill differentiation. The combination of different Oil-Slick Information Descriptors presents comparable discrimination accuracies. Among 8 non-linear transformations, the Logarithm and Cube Root normalizations disclose the most effective discrimination power of almost 70%. Our refined analysis corroborates and consolidates our earlier findings, providing a firmer basis and useful accuracies of the seep-spill discrimination practice using information acquired with space-borne surveillance systems based on Synthetic Aperture Radars.
- Published
- 2018
- Full Text
- View/download PDF
33. The Accuracies of Himawari-8 and MTSAT-2 Sea-Surface Temperatures in the Tropical Western Pacific Ocean
- Author
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Angela L. Ditri, Peter J. Minnett, Yang Liu, Katherine Kilpatrick, and Ajoy Kumar
- Subjects
sea surface temperatures ,geostationary satellite ,infrared ,tropical western Pacific Ocean ,the Great Barrier Reef ,accuracy ,Science - Abstract
Over several decades, improving the accuracy of Sea-Surface Temperatures (SSTs) derived from satellites has been a subject of intense research, and continues to be so. Knowledge of the accuracy of the SSTs is critical for weather and climate predictions, and many research and operational applications. In 2015, the operational Japanese MTSAT-2 geostationary satellite was replaced by the Himawari-8, which has a visible and infrared imager with higher spatial and temporal resolutions than its predecessor. In this study, data from both satellites during a three-month overlap period were compared with subsurface in situ temperature measurements from the Tropical Atmosphere Ocean (TAO) array and self-recording thermometers at the depths of corals of the Great Barrier Reef. Results show that in general the Himawari-8 provides more accurate SST measurements compared to those from MTSAT-2. At various locations, where in situ measurements were taken, the mean Himawari-8 SST error shows an improvement of ~0.15 K. Sources of the differences between the satellite-derived SST and the in situ temperatures were related to wind speed and diurnal heating.
- Published
- 2018
- Full Text
- View/download PDF
34. The GODAE High-Resolution Sea Surface Temperature Project
- Author
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Craig J. Donlon, Kenneth S. Casey, Ian S. Robinson, Chelle L. Gentemann, Richard W. Reynolds, Ian Barton, Olivier Arino, John Stark, Nick Rayner, Pierre LeBorgne, David Poulter, Jorge Vazquez-Cuervo, Edward Armstrong, Helen Beggs, David Llewellyn-Jones, Peter J. Minnett, Christopher J. Merchant, and Robert Evans
- Subjects
GODAE ,GHRSST-PP ,sea surface temperature ,Oceanography ,GC1-1581 - Abstract
Sea surface temperature (SST) measurements are required by operational ocean and atmospheric forecasting systems to constrain modeled upper ocean circulation and thermal structure. The Global Ocean Data Assimilation Experiment (GODAE) High Resolution SST Pilot Project (GHRSST-PP) was initiated to address these needs by coordinating the provision of accurate, high-resolution, SST products for the global domain. The pilot project is now complete, but activities continue within the Group for High Resolution SST (GHRSST). The pilot project focused on harmonizing diverse satellite and in situ data streams that were indexed, processed, quality controlled, analyzed, and documented within a Regional/Global Task Sharing (R/GTS) framework implemented in an internationally distributed manner. Data with meaningful error estimates developed within GHRSST are provided by services within R/GTS. Currently, several terabytes of data are processed at international centers daily, creating more than 25 gigabytes of product. Ensemble SST analyses together with anomaly SST outputs are generated each day, providing confidence in SST analyses via diagnostic outputs. Diagnostic data sets are generated and Web interfaces are provided to monitor the quality of observation and analysis products. GHRSST research and development projects continue to tackle problems of instrument calibration, algorithm development, diurnal variability, skin temperature deviation, and validation/verification of GHRSST products. GHRSST also works closely with applications and users, providing a forum for discussion and feedback between SST users and producers on a regular basis. All data within the GHRSST R/GTS framework are freely available. This paper reviews the progress of GHRSST-PP, highlighting achievements that have been fundamental to the success of the pilot project.
- Published
- 2009
35. MISST: The Multi-Sensor Improved Sea Surface Temperature Project
- Author
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Charlie N. Barron, Lew Gramer, James D. Doyle, Yi Jin, James Cummings, Mark DeMaria, Joseph Sienkiewicz, Chelle L. Gentemann, Peter J. Minnett, Kenneth S. Casey, and Craig J. Donlon
- Subjects
NOPP ,GHRSST ,sea surface temperature ,global weather prediction ,marine weather ,Oceanography ,GC1-1581 - Abstract
Sea surface temperature (SST) measurements are vital to global weather prediction, climate change studies, fisheries management, and a wide range of other applications. Measurements are taken by several satellites carrying infrared and microwave radiometers, moored buoys, drifting buoys, and ships. Collecting all these measurements together and producing global maps of SST has been a difficult endeavor due in part to different data formats, data location and accessibility, and lack of measurement error estimates. The need for a uniform approach to SST measurements and estimation of measurement errors resulted in the formation of the international Global Ocean Data Assimilation Experiment (GODAE) High Resolution SST Pilot Project (GHRSST-PP). Projects were developed in Japan, Europe, and Australia. Simultaneously, in the United States, the Multi-sensor Improved SST (MISST) project was initiated. Five years later, the MISST project has produced satellite SST data from nine satellites in an identical format with ancillary information and estimates of measurement error. Use of these data in global SST analyses has been improved through research into modeling of the ocean surface skin layer and upper ocean diurnal heating. These data and research results have been used by several groups within MISST to produce high-resolution global maps of SSTs, which have been shown to improve tropical cyclone prediction. Additionally, the new SSTs are now used operationally for marine weather warnings and forecasts.
- Published
- 2009
36. The Global Ocean Data Assimilation Experiment High-resolution Sea Surface Temperature Pilot Project
- Author
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Donlon, C., Robinson, I., Casey, K. S., Vazquez-Cuervo, J., Armstrong, E., Arino, O., Gentemann, C., May, D., LeBorgne, P., Piollé, J., Barton, I., Beggs, H., Poulter, D. J. S., Merchant, C. J., Bingham, A., Heinz, S., Harris, A., Wick, G., Emery, B., Minnett, P., Evans, R., Llewellyn-Jones, D., Mutlow, C., Reynolds, R. W., Kawamura, H., and Rayner, N.
- Published
- 2007
37. Aspects of Oceanographic Variability Observed from Thermistor Chains on Free-Drifting Buoys
- Author
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Minnett, P. J., Hopkins, T. S., Potter, John, editor, and Warn-Varnas, Alex, editor
- Published
- 1991
- Full Text
- View/download PDF
38. Toward Improved Validation of Satellite Sea Surface Skin Temperature Measurements for Climate Research
- Author
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Donlon, C. J., Minnett, P. J., Gentemann, C., Nightingale, T. J., Barton, I. J., Ward, B., and Murray, M. J.
- Published
- 2002
39. Play Behavior and Communication between Deaf and Hard of Hearing Children and Their Hearing Peers in an Integrated Preschool.
- Author
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Minnett, Ann
- Abstract
Sixty preschool children (30 deaf and hard of hearing, 30 hearing) were observed in their integrated school during learning center activities and outdoor play. Half experienced auditory communication and half total communication modes. Although all children preferred to play and communicate with same-hearing status children, 63% did communicate with other-hearing status children. (Author/DB)
- Published
- 1994
40. ESTIMATING POLYNYA CLOUDINESS
- Author
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Erica L Key and Peter J Minnett
- Subjects
Oceanography ,GC1-1581 ,Zoology ,QL1-991 - Abstract
Global cloudiness distributions, though an important component in radiative and hydrological budgets, are neither adequately known nor easily retrieved by the spatial and spectral resolutions afforded by current satellite instrumentation. At high latitudes, cold, high albedo surfaces present a particular challenge to cloud retrieval, offering little or no thermal or visible contrast for cloud-ice discrimination. It is in these frequently cloudy and climate-sensitive regions that changing cloud amounts and optical parameters enact the greatest influence, enhancing or suppressing melt through cloud base emission of longwave radiation or scattering of incident shortwave radiation. Polynyas and leads, seasonally ice-free areas characterized by intense air-sea fluxes of heat and moisture, are useful features for exploring the relationships between cloud cover and the underlying surface. Using polar-optimized CASPR (Cloud and Surface Parameter Retrieval) algorithms to process multi-channel AVHRR radiances, cloud amounts, microphysics, and surface forcing are evaluated and validated against in situ measurements collected in several polynyas and leads across the Western Arctic during the years 1992-2000
- Published
- 2004
41. Satellite Infrared Scanning Radiometers — AVHRR and ATSR/M
- Author
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Minnett, P. J. and Vaughan, Robin A., editor
- Published
- 1990
- Full Text
- View/download PDF
42. Learning Disabilities and Social Competence: A Social Ecological Perspective.
- Author
-
Coleman, J. Michael and Minnett, Ann M.
- Abstract
Measures of the social and academic competence of 73 children with learning disabilities in grades 3-6 indicated that subjects differed from children without disabilities on virtually all indexes of academic competence, regardless of social status. Most social differences were linked to the child's peer status, independent of disability. (Author/JDD)
- Published
- 1993
43. Exploratory Data Analysis of Synthetic Aperture Radar (SAR) Measurements to Distinguish the Sea Surface Expressions of Naturally-Occurring Oil Seeps from Human-Related Oil Spills in Campeche Bay (Gulf of Mexico)
- Author
-
Gustavo de Araújo Carvalho, Peter J. Minnett, Fernando Pellon de Miranda, Luiz Landau, and Eduardo Tavares Paes
- Subjects
Exploratory Data Analysis (EDA) ,sea surface monitoring ,oil slick type differentiation ,oil seep ,oil spill ,remote sensing ,Synthetic Aperture Radar (SAR) ,RADARSAT-2 ,Geography (General) ,G1-922 - Abstract
An Exploratory Data Analysis (EDA) aims to use Synthetic Aperture Radar (SAR) measurements for discriminating between two oil slick types observed on the sea surface: naturally-occurring oil seeps versus human-related oil spills—the use of satellite sensors for this task is poorly documented in scientific literature. A long-term RADARSAT dataset (2008–2012) is exploited to investigate oil slicks in Campeche Bay (Gulf of Mexico). Simple Classification Algorithms to distinguish the oil slick type are designed based on standard multivariate data analysis techniques. Various attributes of geometry, shape, and dimension that describe the oil slick Size Information are combined with SAR-derived backscatter coefficients—sigma-(σo), beta-(βo), and gamma-(γo) naught. The combination of several of these characteristics is capable of distinguishing the oil slick type with ~70% of overall accuracy, however, the sole and simple use of two specific oil slick’s Size Information (i.e., area and perimeter) is equally capable of distinguishing seeps from spills. The data mining exercise of our EDA promotes a novel idea bridging petroleum pollution and remote sensing research, thus paving the way to further investigate the satellite synoptic view to express geophysical differences between seeped and spilled oil observed on the sea surface for systematic use.
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- 2017
- Full Text
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44. Observations of Sea-Surface Temperature for Climate Research [and Discussion]
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Harries, J. E., Llewellyn-Jones, D. T., Minnett, P. J., Saunders, R. W., Zavody, A. M., Wadhams, P., Taylor, P. K., and Houghton, J. T.
- Published
- 1983
45. The Structure of a Weak Thermohaline Front [and Discussion]
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Minnett, P. J., Pollard, R. T., Collins, D. S., Horch, A., Knoll, M., Gregg, M. C., Woods, J. D., Thorpe, S. A., and Briscoe, M. G.
- Published
- 1983
46. High Latitude Sea Surface Skin Temperatures Derived From Saildrone Infrared Measurements
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Jia, Chong, Minnett, Peter J., Szczodrak, Malgorzata, and Izaguirre, Miguel
- Abstract
From May 15 to October 11, 2019, six Saildrone uncrewed surface vehicles (USVs) were deployed for 150-day cruises collecting a suite of atmospheric and oceanographic measurements from Dutch Harbor, Alaska, transiting the Bering Strait into the Chukchi Sea and the Arctic Ocean. Two Saildrones funded by the National Aeronautics and Space Administration (NASA), SD-1036 and SD-1037, were equipped with infrared (IR) radiation pyrometers in a “unicorn” structure on the deck for the determination of the ocean sea surface skin temperature (SST
$_{\mathrm {skin}}$ - Published
- 2023
- Full Text
- View/download PDF
47. Towards more biologically-plausible computational models for cognition, texture classification, and network replication
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Minnett, Rupert Charles James
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UCSD Dissertations, Academic Electrical engineering (Signal and image processing). (Discipline) - Abstract
Neuroscience and machine learning often operate at two ends of a spectrum. The former sometimes finds itself entrenched in the details of experimentation, and the latter sometimes finds itself drifting into the expanse of theory. Both fields can mutually coexist, and when they do, have produced invaluable results in computational neuroscience towards more plausible models of biological solutions. This dissertation presents two detailed investigations into the benefits of this interdisciplinary field: a model for cognition and a model for vision. Experiments during these investigations led us to a third result: a new learning approach called neural network tomography. We introduce our universal theory of cognition, Confabulation Theory, and discuss its biological plausibility. Confabulation Theory posits that the cerebral cortex, in conjunction with the thalamus, is implementing a repeated functional architecture of thalamocortical modules, each encoding one attribute which an object in the individual's mental universe may possess. These modules are interconnected with concurrence statistics called knowledge links, are capable of confabulating a state, and are carefully controlled with action commands. We use Confabulation Theory to build a model for natural language processing and present striking results in sentence generation with context. Subsequently, we focus on the task of texture classification, which we argue is a more primitive operation than object recognition, and therefore, appropriate for investigation with the goal of elucidating biology's solution for processing visual stimuli. We develop a hierarchical model for texture classification, carefully informed by neuroscience results, and demonstrate state-of-the-art performance on a challenging texture classification dataset in the context of our human psychophysical experiment. Finally, we survey existing methods in neural network learning and propose a new approach with several valuable theoretical advantages. By rephrasing the task of function approximation as replicating the topology and weights of an existing universal approximator network, we show that several of the drawbacks of classical backpropagation learning can be avoided. We define a new objective function, mean squared curvature (MSC), and demonstrate that minimizing the MSC of the difference between the networks during the replication process produces favorable results and allows networks to be reverse-engineered iteratively
- Published
- 2012
48. Environmental controls, oceanography and population dynamics of pathogens and harmful algal blooms: connecting sources to human exposure
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Minnett Peter, McGillicuddy Dennis J, Lerczak James, Laws Ed, Holland Fred, Hitchcock Gary, Dusek Eva, Bienfang Paul, Dyble Julianne, Moore Stephanie K, O'Kelly Charles, Solo-Gabriele Helena, and Wang John D
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Industrial medicine. Industrial hygiene ,RC963-969 ,Public aspects of medicine ,RA1-1270 - Abstract
Abstract Coupled physical-biological models are capable of linking the complex interactions between environmental factors and physical hydrodynamics to simulate the growth, toxicity and transport of infectious pathogens and harmful algal blooms (HABs). Such simulations can be used to assess and predict the impact of pathogens and HABs on human health. Given the widespread and increasing reliance of coastal communities on aquatic systems for drinking water, seafood and recreation, such predictions are critical for making informed resource management decisions. Here we identify three challenges to making this connection between pathogens/HABs and human health: predicting concentrations and toxicity; identifying the spatial and temporal scales of population and ecosystem interactions; and applying the understanding of population dynamics of pathogens/HABs to management strategies. We elaborate on the need to meet each of these challenges, describe how modeling approaches can be used and discuss strategies for moving forward in addressing these challenges.
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- 2008
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49. Analysis of pathfinder SST algorithm for global and regional conditions
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Kumar, Ajoy, Minnett, Peter, Podestá, Guillermo, Evans, Robert, and Kilpatrick, Katherine
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- 2000
- Full Text
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50. Fluorescence Switching for Temperature Sensing in Water.
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Zheng, Yeting, Meana, Yasniel, Mazza, Mercedes M. A., Baker, James D., Minnett, Peter J., and Raymo, Françisco M.
- Published
- 2022
- Full Text
- View/download PDF
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