Search Results
You are looking at 1 - 8 of 8 items for
- Author or Editor: T. Sikora x
- Refine by Access: All Content x
Abstract
The viability of synthetic aperture radar (SAR) as a tool for finescale marine meteorological surface analyses of synoptic-scale fronts is demonstrated. In particular, it is shown that SAR can reveal the presence of, and the mesoscale and microscale substructures associated with, synoptic-scale cold fronts, warm fronts, occluded fronts, and secluded fronts. The basis for these findings is the analysis of some 6000 RADARSAT-1 SAR images from the Gulf of Alaska and from off the east coast of North America. This analysis yielded 158 cases of well-defined frontal signatures: 22 warm fronts, 37 cold fronts, 3 stationary fronts, 32 occluded fronts, and 64 secluded fronts. The potential synergies between SAR and a range of other data sources are discussed for representative fronts of each type.
Abstract
The viability of synthetic aperture radar (SAR) as a tool for finescale marine meteorological surface analyses of synoptic-scale fronts is demonstrated. In particular, it is shown that SAR can reveal the presence of, and the mesoscale and microscale substructures associated with, synoptic-scale cold fronts, warm fronts, occluded fronts, and secluded fronts. The basis for these findings is the analysis of some 6000 RADARSAT-1 SAR images from the Gulf of Alaska and from off the east coast of North America. This analysis yielded 158 cases of well-defined frontal signatures: 22 warm fronts, 37 cold fronts, 3 stationary fronts, 32 occluded fronts, and 64 secluded fronts. The potential synergies between SAR and a range of other data sources are discussed for representative fronts of each type.
Abstract
Two distinct backscatter regimes are seen on a European remote sensing satellite ERS-1 C-band (5.6 cm) synthetic aperture radar (SAR) image of the sea surface during a time of fair synoptic-scale weather conditions. One backscatter regime is mottled. In contrast to that, the second backscatter regime is marbled.
The authors hypothesize that the mottled backscatter pattern is a characteristic SAR backscatter pattern linked to the presence of the convective (i.e., statically unstable/convective-eddy containing) marine atmospheric boundary layer (CMABL) and can be used to help determine CMABL structure [convective-eddy type (cellular convection versus longitudinal rolls), eddy wavelength, and CMABL depth (via mixed-layer similarity theory for aspect ratio)]. The hypothesis linking the presence and structure of the CMABL to the mottled backscatter pattern on SAR imagery is validated by analyzing data from a number of sources gathered in the vicinity of the boundary between the mottled and marbled regimes on the SAR image.
Abstract
Two distinct backscatter regimes are seen on a European remote sensing satellite ERS-1 C-band (5.6 cm) synthetic aperture radar (SAR) image of the sea surface during a time of fair synoptic-scale weather conditions. One backscatter regime is mottled. In contrast to that, the second backscatter regime is marbled.
The authors hypothesize that the mottled backscatter pattern is a characteristic SAR backscatter pattern linked to the presence of the convective (i.e., statically unstable/convective-eddy containing) marine atmospheric boundary layer (CMABL) and can be used to help determine CMABL structure [convective-eddy type (cellular convection versus longitudinal rolls), eddy wavelength, and CMABL depth (via mixed-layer similarity theory for aspect ratio)]. The hypothesis linking the presence and structure of the CMABL to the mottled backscatter pattern on SAR imagery is validated by analyzing data from a number of sources gathered in the vicinity of the boundary between the mottled and marbled regimes on the SAR image.
Abstract
The Canadian Forces Meteorology and Oceanography Center produces a near-daily ocean feature analysis, based on sea surface temperature (SST) images collected by spaceborne radiometers, to keep the fleet informed of the location of tactically important ocean features. Ubiquitous cloud cover hampers these data. In this paper, a methodology for the identification of SST front signatures in cloud-independent synthetic aperture radar (SAR) images is described. Accurate identification of ocean features in SAR images, although attainable to an experienced analyst, is a difficult process to automate. As a first attempt, the authors aimed to discriminate between signatures of SST fronts and those caused by all other processes. Candidate SST front signatures were identified in Radarsat-2 images using a Canny edge detector. A feature vector of textural and contextual measures was constructed for each candidate edge, and edges were validated by comparison with coincident SST images. Each candidate was classified as being an SST front signature or the signature of another process using logistic regression. The resulting probability that a candidate was correctly classified as an SST front signature was between 0.50 and 0.70. The authors concluded that improvement in classification accuracy requires a set of measures that can differentiate between signatures of SST fronts and those of certain atmospheric phenomena and that a search for such measures should include a wider range of computational methods than was considered. As such, this work represents a step toward the goal of a general ocean feature classification algorithm.
Abstract
The Canadian Forces Meteorology and Oceanography Center produces a near-daily ocean feature analysis, based on sea surface temperature (SST) images collected by spaceborne radiometers, to keep the fleet informed of the location of tactically important ocean features. Ubiquitous cloud cover hampers these data. In this paper, a methodology for the identification of SST front signatures in cloud-independent synthetic aperture radar (SAR) images is described. Accurate identification of ocean features in SAR images, although attainable to an experienced analyst, is a difficult process to automate. As a first attempt, the authors aimed to discriminate between signatures of SST fronts and those caused by all other processes. Candidate SST front signatures were identified in Radarsat-2 images using a Canny edge detector. A feature vector of textural and contextual measures was constructed for each candidate edge, and edges were validated by comparison with coincident SST images. Each candidate was classified as being an SST front signature or the signature of another process using logistic regression. The resulting probability that a candidate was correctly classified as an SST front signature was between 0.50 and 0.70. The authors concluded that improvement in classification accuracy requires a set of measures that can differentiate between signatures of SST fronts and those of certain atmospheric phenomena and that a search for such measures should include a wider range of computational methods than was considered. As such, this work represents a step toward the goal of a general ocean feature classification algorithm.
Abstract
Automated classification of the signatures of atmospheric and oceanic processes in synthetic aperture radar (SAR) images of the ocean surface has been a difficult problem, partly because different processes can produce signatures that are very similar in appearance. For example, brightness fronts that are the signatures of horizontal wind shear caused by atmospheric processes that occur independently of properties of the ocean (WIN herein) often appear very similar to brightness fronts that are signatures of sea surface temperature (SST) fronts (SST herein). Using Moderate Resolution Imaging Spectroradiometer (MODIS)-derived SST for validation, 302 SAR SST and 193 SAR WIN signatures were collected from over 250 RADARSAT-2 images of the Gulf Stream region using a Canny edge detector. A vector consisting of textural and contextual features was extracted from each signature and used to train and test logistic regression, maximum likelihood, and binary tree classifiers. Following methods proven effective in the analysis of SAR images of sea ice, textural features included those computed from the gray-level co-occurrence matrix for regions along and astride each signature. Contextual features consisted of summaries of the wind vector field near each signature. Results indicate that signatures labeled SST can be automatically discriminated from signatures labeled WIN using the mean wind direction with respect to a brightness front with an accuracy of between 80% and 90%.
Abstract
Automated classification of the signatures of atmospheric and oceanic processes in synthetic aperture radar (SAR) images of the ocean surface has been a difficult problem, partly because different processes can produce signatures that are very similar in appearance. For example, brightness fronts that are the signatures of horizontal wind shear caused by atmospheric processes that occur independently of properties of the ocean (WIN herein) often appear very similar to brightness fronts that are signatures of sea surface temperature (SST) fronts (SST herein). Using Moderate Resolution Imaging Spectroradiometer (MODIS)-derived SST for validation, 302 SAR SST and 193 SAR WIN signatures were collected from over 250 RADARSAT-2 images of the Gulf Stream region using a Canny edge detector. A vector consisting of textural and contextual features was extracted from each signature and used to train and test logistic regression, maximum likelihood, and binary tree classifiers. Following methods proven effective in the analysis of SAR images of sea ice, textural features included those computed from the gray-level co-occurrence matrix for regions along and astride each signature. Contextual features consisted of summaries of the wind vector field near each signature. Results indicate that signatures labeled SST can be automatically discriminated from signatures labeled WIN using the mean wind direction with respect to a brightness front with an accuracy of between 80% and 90%.
The Royal Canadian Navy produces a semiweekly map of major water mass boundaries in the Western North Atlantic using temperature measurements from several data sources, including satellite sea surface temperature (SST) images from the Advanced Very High Resolution Radiometer (AVHRR). Temporal–spatial detail that can be provided by AVHRR of the location of important SST boundaries such as the Gulf Stream North Wall is limited due to pervasive cloud cover. The ability of satellite-borne synthetic aperture radar (SAR) to image SST front signatures unrestrained by cloud cover makes it a potentially significant additional data source. The Spaceborne Ocean Intelligence Network project has developed an automated procedure to detect candidate SST front signatures in RADARSAT-2 SAR images of the ocean surface and classify them with greater than 80% accuracy.
The Royal Canadian Navy produces a semiweekly map of major water mass boundaries in the Western North Atlantic using temperature measurements from several data sources, including satellite sea surface temperature (SST) images from the Advanced Very High Resolution Radiometer (AVHRR). Temporal–spatial detail that can be provided by AVHRR of the location of important SST boundaries such as the Gulf Stream North Wall is limited due to pervasive cloud cover. The ability of satellite-borne synthetic aperture radar (SAR) to image SST front signatures unrestrained by cloud cover makes it a potentially significant additional data source. The Spaceborne Ocean Intelligence Network project has developed an automated procedure to detect candidate SST front signatures in RADARSAT-2 SAR images of the ocean surface and classify them with greater than 80% accuracy.
Abstract
Predicting the inland penetration of lake-effect long-lake-axis-parallel (LLAP) snowbands is crucial to public safety because LLAP bands can produce hazardous weather well downwind of the parent lake. Accordingly, hypotheses for the variation in inland penetration of LLAP-band radar echoes (InPen) are formulated and tested. The hypothesis testing includes an examination of statistical relationships between environmental variables and InPen for 34 snapshots of LLAP bands observed during the Ontario Winter Lake-effect Systems (OWLeS) field campaign. Several previously proposed predictors of LLAP-band formation or InPen demonstrate weak correlations with InPen during OWLeS. A notable exception is convective boundary layer (CBL) depth, which is strongly correlated with InPen. In addition to CBL depth, InPen is strongly correlated with cold-air advection in the upper portion of the CBL, suggesting that boundary layer destabilization produced by vertically differential cold-air advection may be an important inland power source for preexisting LLAP bands. This power production is quantified through atmospheric energetics and the resulting variable, differential thermal advection power (DTAP), yields reasonably skillful predictions of InPen. Nevertheless, an InPen model developed using DTAP is outperformed by an empirical model combining CBL depth and potential temperature advection in the upper portion of the CBL. This two-variable model explains 76% of the observed InPen variance when tested on independent data. Finally, implications for operational forecasting of InPen are discussed.
Abstract
Predicting the inland penetration of lake-effect long-lake-axis-parallel (LLAP) snowbands is crucial to public safety because LLAP bands can produce hazardous weather well downwind of the parent lake. Accordingly, hypotheses for the variation in inland penetration of LLAP-band radar echoes (InPen) are formulated and tested. The hypothesis testing includes an examination of statistical relationships between environmental variables and InPen for 34 snapshots of LLAP bands observed during the Ontario Winter Lake-effect Systems (OWLeS) field campaign. Several previously proposed predictors of LLAP-band formation or InPen demonstrate weak correlations with InPen during OWLeS. A notable exception is convective boundary layer (CBL) depth, which is strongly correlated with InPen. In addition to CBL depth, InPen is strongly correlated with cold-air advection in the upper portion of the CBL, suggesting that boundary layer destabilization produced by vertically differential cold-air advection may be an important inland power source for preexisting LLAP bands. This power production is quantified through atmospheric energetics and the resulting variable, differential thermal advection power (DTAP), yields reasonably skillful predictions of InPen. Nevertheless, an InPen model developed using DTAP is outperformed by an empirical model combining CBL depth and potential temperature advection in the upper portion of the CBL. This two-variable model explains 76% of the observed InPen variance when tested on independent data. Finally, implications for operational forecasting of InPen are discussed.
Abstract
Lake-effect snowstorms are often observed to manifest as dominant bands, commonly produce heavy localized snowfall, and may extend large distances inland, resulting in hazards and high societal impact. Some studies of dominant bands have documented concomitant environmental baroclinity (i.e., baroclinity occurring at a scale larger than the width of the parent lake), but the interaction of this baroclinity with the inland structure of dominant bands has been largely unexplored. In this study, the thermodynamic environment and thermodynamic and kinematic structure of simulated dominant bands are examined using WRF reanalyses at 3-km horizontal resolution and an innovative technique for selecting the most representative member from the WRF ensemble. Three reanalysis periods are selected from the Ontario Winter Lake-effect Systems (OWLeS) field campaign, encompassing 185 simulation hours, including 155 h in which dominant bands are identified. Environmental baroclinity is commonly observed during dominant-band periods and occurs in both the north–south and east–west directions. Sources of this baroclinity are identified and discussed. In addition, case studies are conducted for simulation hours featuring weak and strong along-band environmental baroclinity, resulting in weak and strong inland extent, respectively. These contrasting cases offer insight into one mechanism by which along-band environmental baroclinity can influence the inland structure and intensity of dominant bands: in the case with strong environmental baroclinity, inland portions of this band formed under weak instability and therefore exhibit slow overturning, enabling advection far inland under strong winds, whereas the nearshore portion forms under strong instability, and the enhanced overturning eventually leads to the demise of the inland portion of the band.
Abstract
Lake-effect snowstorms are often observed to manifest as dominant bands, commonly produce heavy localized snowfall, and may extend large distances inland, resulting in hazards and high societal impact. Some studies of dominant bands have documented concomitant environmental baroclinity (i.e., baroclinity occurring at a scale larger than the width of the parent lake), but the interaction of this baroclinity with the inland structure of dominant bands has been largely unexplored. In this study, the thermodynamic environment and thermodynamic and kinematic structure of simulated dominant bands are examined using WRF reanalyses at 3-km horizontal resolution and an innovative technique for selecting the most representative member from the WRF ensemble. Three reanalysis periods are selected from the Ontario Winter Lake-effect Systems (OWLeS) field campaign, encompassing 185 simulation hours, including 155 h in which dominant bands are identified. Environmental baroclinity is commonly observed during dominant-band periods and occurs in both the north–south and east–west directions. Sources of this baroclinity are identified and discussed. In addition, case studies are conducted for simulation hours featuring weak and strong along-band environmental baroclinity, resulting in weak and strong inland extent, respectively. These contrasting cases offer insight into one mechanism by which along-band environmental baroclinity can influence the inland structure and intensity of dominant bands: in the case with strong environmental baroclinity, inland portions of this band formed under weak instability and therefore exhibit slow overturning, enabling advection far inland under strong winds, whereas the nearshore portion forms under strong instability, and the enhanced overturning eventually leads to the demise of the inland portion of the band.
A collaboration between faculty and students at six universities in a project called Weather in a Tank is described, in which ways of teaching atmosphere, ocean, and climate dynamics are explored that bring students into contact with real fluids and fundamental ideas. Exploiting the use of classic rotating laboratory experiments, real-time meteorological data and associated theory, teaching tools, curricular, and evaluation materials have been developed that focus on fundamental aspects of atmospheric and oceanographic dynamics for use in undergraduate teaching. The intent of the project is to help students learn how to move between phenomena in the real world, theory, and models.
A collaboration between faculty and students at six universities in a project called Weather in a Tank is described, in which ways of teaching atmosphere, ocean, and climate dynamics are explored that bring students into contact with real fluids and fundamental ideas. Exploiting the use of classic rotating laboratory experiments, real-time meteorological data and associated theory, teaching tools, curricular, and evaluation materials have been developed that focus on fundamental aspects of atmospheric and oceanographic dynamics for use in undergraduate teaching. The intent of the project is to help students learn how to move between phenomena in the real world, theory, and models.
