scholarly journals Temporal Variability of the Large-Scale Geostrophic Surface Velocity in the Northeast Pacific*

1997 ◽  
Vol 27 (10) ◽  
pp. 2288-2297 ◽  
Author(s):  
P. van Meurs ◽  
P. P. Niiler
Keyword(s):  
Temporal Variability ◽  
Large Scale ◽  
Surface Velocity ◽  
Northeast Pacific

scholarly journals On the origin of variable structures in the winds of hot luminous stars

2013 ◽  
Vol 440 (1) ◽  
pp. 2-9 ◽  
Author(s):  
Yannick J. L. Michaux ◽  
Anthony F. J. Moffat ◽  
André-Nicolas Chené ◽  
Nicole St-Louis
Keyword(s):  
Magnetic Field ◽  
Empirical Evidence ◽  
Temporal Variability ◽  
Large Scale ◽  
Small Scale ◽  
Ionization Zone ◽  
Corotating Interaction Regions ◽  
Wind Variability ◽  
Global Magnetic Field ◽  
Interaction Regions

Abstract Examination of the temporal variability properties of several strong optical recombination lines in a large sample of Galactic Wolf–Rayet (WR) stars reveals possible trends, especially in the more homogeneous WC than the diverse WN subtypes, of increasing wind variability with cooler subtypes. This could imply that a serious contender for the driver of the variations is stochastic, magnetic subsurface convection associated with the 170 kK partial-ionization zone of iron, which should occupy a deeper and larger zone of greater mass in cooler WR subtypes. This empirical evidence suggests that the heretofore proposed ubiquitous driver of wind variability, radiative instabilities, may not be the only mechanism playing a role in the stochastic multiple small-scaled structures seen in the winds of hot luminous stars. In addition to small-scale stochastic behaviour, subsurface convection guided by a global magnetic field with localized emerging loops may also be at the origin of the large-scale corotating interaction regions as seen frequently in O stars and occasionally in the winds of their descendant WR stars.

Seeding metrics for image velocimetry performances in rivers

2021 ◽  
Author(s):  
Silvano Fortunato Dal Sasso ◽  
Alonso Pizarro ◽  
Sophie Pearce ◽  
Ian Maddock ◽  
Matthew T. Perks ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Optical Sensors ◽  
Large Scale ◽  
Surface Flow ◽  
Surface Velocity ◽  
Seeding Density ◽  
Earth System ◽  
Science Data ◽  
Image Velocimetry ◽  
Hydraulic Conditions

<p>Optical sensors coupled with image velocimetry techniques are becoming popular for river monitoring applications. In this context, new opportunities and challenges are growing for the research community aimed to: i) define standardized practices and methodologies; and ii) overcome some recognized uncertainty at the field scale. At this regard, the accuracy of image velocimetry techniques strongly depends on the occurrence and distribution of visible features on the water surface in consecutive frames. In a natural environment, the amount, spatial distribution and visibility of natural features on river surface are continuously challenging because of environmental factors and hydraulic conditions. The dimensionless seeding distribution index (SDI), recently introduced by Pizarro et al., 2020a,b and Dal Sasso et al., 2020, represents a metric based on seeding density and spatial distribution of tracers for identifying the best frame window (FW) during video footage. In this work, a methodology based on the SDI index was applied to different study cases with the Large Scale Particle Image Velocimetry (LSPIV) technique. Videos adopted are taken from the repository recently created by the COST Action Harmonious, which includes 13 case study across Europe and beyond for image velocimetry applications (Perks et al., 2020). The optimal frame window selection is based on two criteria: i) the maximization of the number of frames and ii) the minimization of SDI index. This methodology allowed an error reduction between 20 and 39% respect to the entire video configuration. This novel idea appears suitable for performing image velocimetry in natural settings where environmental and hydraulic conditions are extremely challenging and particularly useful for real-time observations from fixed river-gauged stations where an extended number of frames are usually recorded and analyzed.</p><p> </p><p><strong>References </strong></p><p>Dal Sasso S.F., Pizarro A., Manfreda S., Metrics for the Quantification of Seeding Characteristics to Enhance Image Velocimetry Performance in Rivers. Remote Sensing, 12, 1789 (doi: 10.3390/rs12111789), 2020.</p><p>Perks M. T., Dal Sasso S. F., Hauet A., Jamieson E., Le Coz J., Pearce S., …Manfreda S, Towards harmonisation of image velocimetry techniques for river surface velocity observations. Earth System Science Data, https://doi.org/10.5194/essd-12-1545-2020, 12(3), 1545 – 1559, 2020.</p><p>Pizarro A., Dal Sasso S.F., Manfreda S., Refining image-velocimetry performances for streamflow monitoring: Seeding metrics to errors minimisation, Hydrological Processes, (doi: 10.1002/hyp.13919), 1-9, 2020.</p><p>Pizarro A., Dal Sasso S.F., Perks M. and Manfreda S., Identifying the optimal spatial distribution of tracers for optical sensing of stream surface flow, Hydrology and Earth System Sciences, 24, 5173–5185, (10.5194/hess-24-5173-2020), 2020.</p>

scholarly journals Seasonal prediction of US summertime ozone using statistical analysis of large scale climate patterns

2017 ◽  
Vol 114 (10) ◽  
pp. 2491-2496 ◽  
Author(s):  
Lu Shen ◽  
Loretta J. Mickley
Keyword(s):  
United States ◽  
Large Scale ◽  
Surface Ozone ◽  
Tropical Atlantic ◽  
Eastern United States ◽  
Northeast Pacific ◽  
Ozone Concentrations ◽  
Climate Patterns ◽  
Ocean Atmosphere ◽  
Ozone Episodes

We develop a statistical model to predict June–July–August (JJA) daily maximum 8-h average (MDA8) ozone concentrations in the eastern United States based on large-scale climate patterns during the previous spring. We find that anomalously high JJA ozone in the East is correlated with these springtime patterns: warm tropical Atlantic and cold northeast Pacific sea surface temperatures (SSTs), as well as positive sea level pressure (SLP) anomalies over Hawaii and negative SLP anomalies over the Atlantic and North America. We then develop a linear regression model to predict JJA MDA8 ozone from 1980 to 2013, using the identified SST and SLP patterns from the previous spring. The model explains ∼45% of the variability in JJA MDA8 ozone concentrations and ∼30% variability in the number of JJA ozone episodes (>70 ppbv) when averaged over the eastern United States. This seasonal predictability results from large-scale ocean–atmosphere interactions. Warm tropical Atlantic SSTs can trigger diabatic heating in the atmosphere and influence the extratropical climate through stationary wave propagation, leading to greater subsidence, less precipitation, and higher temperatures in the East, which increases surface ozone concentrations there. Cooler SSTs in the northeast Pacific are also associated with more summertime heatwaves and high ozone in the East. On average, models participating in the Atmospheric Model Intercomparison Project fail to capture the influence of this ocean–atmosphere interaction on temperatures in the eastern United States, implying that such models would have difficulty simulating the interannual variability of surface ozone in this region.

Dynamics of Aeolian Sand Streamers and Airflow Turbulence over a Beach Surface

2021 ◽  
Author(s):  
Andreas Baas
Keyword(s):  
Boundary Layer ◽  
Turbulent Boundary Layer ◽  
Temporal Variability ◽  
Large Scale ◽  
Sonic Anemometer ◽  
Dynamic Structure ◽  
Sand Transport ◽  
Video Imagery ◽  
Spatio Temporal ◽  
The Impact

<p>Sand transport by wind over granular beds displays dynamic structure and organisation in the form of streamers (aka ‘sand snakes’) that appear, meander and intertwine, and then dissipate as they are advected downwind. These patterns of saltating grain populations are thought to be initiated and controlled by coherent flow structures in the turbulent boundary layer wind that scrape over the bed surface raking up sand into entrainment. Streamer behaviour is thus fundamental to understanding sand transport dynamics, in particular its strong spatio-temporal variability, and is equally relevant to granular transport in other geophysical flows (fluvial, submarine).</p><p>This paper presents findings on streamer dynamics and associated wind turbulence observed in a field experiment on a beach, with measurements from 30Hz video-imagery using Large-Scale Particle Image Velocimetry (LS-PIV), combined with 50Hz wind measurements from 3D sonic anemometry and co-located sand transport rate monitoring using an array of laser particle counters (‘Wenglors’), all taking place over an area of ~10 m<sup>2</sup> and over periods of several minutes. The video imagery was used to identify when and where streamers advected past the sonic anemometer and laser sensors so that relationships could be detected between the passage of turbulence structures in the airflow and the length- and time-scales, propagation speeds, and sand transport intensities of associated streamers. The findings form the basis for a phenomenological model of streamer dynamics under turbulent boundary layer flows that predicts the impact of spatio-temporal variability on local measurement of sand transport.</p>

scholarly journals On the multiple time scales of variability in the Northeast Pacific Ocean

Ocean Science ◽  
2009 ◽  
Vol 5 (4) ◽  
pp. 591-605
Author(s):  
R. Tokmakian
Keyword(s):  
Pacific Ocean ◽  
Large Scale ◽  
Southern Oscillation ◽  
Set Covering ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Data Set ◽  
Northeast Pacific ◽  
Energy Signal ◽  
Northeast Pacific Ocean

Abstract. The spatial and temporal sea surface height energy distribution of the Northeast Pacific Ocean is described and discussed. Using an altimetric data set covering 15 years (1993–2007), the energy within the 3–9 month band is primarily located within 10° of the coast. In the Gulf of Alaska, this energy signal is on the shelf, while further south, west of the California/Oregon coast, the significant energy in this band is west of the shelf break. In both cases, it is primarily forced by the local wind. Within the 2–3 year band, the signal reflects energy generated by local changes to the wind stress from large atmospheric shifts indicated by the Pacific North American Index and by advective or propagating processes related to El Niño-Southern Oscillation. Over the two 4–6 year periods within this data set, the change is primarily due to the large scale shift in atmospheric systems north of about 30° N which also affect changes in current strengths. Based on the distribution of the energy signal and its variability, a set of three winter-time indices are suggested to characterize the distinct differences in the SSH anomalies in these areas.

Spatial and temporal variability of East African Kiremt season precipitation and large‐scale teleconnections

2019 ◽  
Vol 40 (2) ◽  
pp. 1241-1254 ◽  
Author(s):  
Daniel Broman ◽  
Balaji Rajagopalan ◽  
Thomas Hopson ◽  
Mekonnen Gebremichael
Keyword(s):  
Temporal Variability ◽  
Large Scale ◽  
Spatial And Temporal Variability ◽  
East African

scholarly journals sUAS-Based Remote Sensing of River Discharge Using Thermal Particle Image Velocimetry and Bathymetric Lidar

2019 ◽  
Vol 11 (19) ◽  
pp. 2317 ◽  
Author(s):  
Paul Kinzel ◽  
Carl Legleiter
Keyword(s):  
Particle Image Velocimetry ◽  
River Discharge ◽  
Water Surface ◽  
Large Scale ◽  
Particle Image ◽  
Field Evaluation ◽  
Remotely Sensed ◽  
Surface Velocity ◽  
Unmanned Aerial Systems ◽  
Image Velocimetry

This paper describes a non-contact methodology for computing river discharge based on data collected from small Unmanned Aerial Systems (sUAS). The approach is complete in that both surface velocity and channel geometry are measured directly under field conditions. The technique does not require introducing artificial tracer particles for computing surface velocity, nor does it rely upon the presence of naturally occurring floating material. Moreover, no prior knowledge of river bathymetry is necessary. Due to the weight of the sensors and limited payload capacities of the commercially available sUAS used in the study, two sUAS were required. The first sUAS included mid-wave thermal infrared and visible cameras. For the field evaluation described herein, a thermal image time series was acquired and a particle image velocimetry (PIV) algorithm used to track the motion of structures expressed at the water surface as small differences in temperature. The ability to detect these thermal features was significant because the water surface lacked floating material (e.g., foam, debris) that could have been detected with a visible camera and used to perform conventional Large-Scale Particle Image Velocimetry (LSPIV). The second sUAS was devoted to measuring bathymetry with a novel scanning polarizing lidar. We collected field measurements along two channel transects to assess the accuracy of the remotely sensed velocities, depths, and discharges. Thermal PIV provided velocities that agreed closely ( R 2 = 0.82 and 0.64) with in situ velocity measurements from an acoustic Doppler current profiler (ADCP). Depths inferred from the lidar closely matched those surveyed by wading in the shallower of the two cross sections ( R 2 = 0.95), but the agreement was not as strong for the transect with greater depths ( R 2 = 0.61). Incremental discharges computed with the remotely sensed velocities and depths were greater than corresponding ADCP measurements by 22% at the first cross section and <1% at the second.

The detection of ionospheric trough with GNSS measurements.

2020 ◽  
Author(s):  
Rafal Sieradzki ◽  
Jacek Paziewski
Keyword(s):  
Plasma Density ◽  
Geomagnetic Activity ◽  
Temporal Variability ◽  
Large Scale ◽  
Auroral Oval ◽  
Initial Assessment ◽  
Phase Measurements ◽  
Main Ionospheric Trough ◽  
Gnss Measurements ◽  
And Shifts

&lt;p&gt;The main ionospheric trough represents a large scale depletion of plasma density elongated in longitude, which is typically observed at the boundary between high- and mid-latitude ionosphere. The trough is characterized&amp;#160; by a steep density gradient in a poleward direction and gradual on the equatorward site. According to the recent studies it begins in the late afternoon, moves equatorward during the night hours and rapidly retreats to higher latitudes at a dawn. Due to the dynamic of auroral oval, this ionospheric feature exhibits a high temporal variability and shifts equatorward during the geomagnetic activity. In this work we demonstrate the initial assessment of the ionospheric trough detection performed with GNSS-based relative STEC values. The basis of this indicator are time series of&amp;#160; geometry-free combination with removed background variations. The separation of these low-term effects is realized with a polynomial fitting applied to the particular arcs of data. Such processed data have an accuracy of phase measurements and provide an epoch-wise information on enhancement/depletion of plasma density. In order to evaluate the applicability of the proposed approach for the trough detection, we have analyzed the state of the ionosphere during different geomagnetic conditions. In our investigations we have used the data from several tens of stations located in the northern hemisphere, what makes possible to provide the comprehensive view of this ionospheric phenomenon. The results have confirmed that the network-derived relative STEC values can be successfully used for the monitoring ionospheric trough. Its signature is more pronounced for expanded auroral oval during increased geomagnetic activity and reach in such case a few TEC units.&amp;#160;&amp;#160; &amp;#160;&amp;#160;&lt;/p&gt;

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