(Collins et al., 2013), we found a small decrease North At- lantic winds in DJF and JJA, as can be seen in Fig. 2. The whiskers on the boxes show the spread of the results across the ensemble members for each scenario. There are decreases of between 1–3 % in the mean wind speed. Projected de- creases in all of the wind speed percentiles (averaged over the North Atlantic) are presented in Gallagher et al. (2016b). Examining the spatial distribution of these changes in Fig. 3 reveals a more diverse picture with regions of increas- ing and decreasing surface winds. This figure shows areal plots of the changes in the seasonal ensemble mean 10 m winds (ensembles of three members for both RCP4.5 and RCP8.5 scenarios) for the future relative to the historical pe- riod. Regions of relative decrease (negative changes, blue) can be seen in the swell generating areas for Ireland in the Northeast Atlantic for all seasons, for both scenarios, with larger decreases in the mean winds under the RCP8.5 sce- nario. This is particularly true for RCP8.5 DJF and JJA sea- sonal means where the decreases are more negatively pro- nounced (darker blue) and there are larger areas of statistical significance based on the following metric: where the change in the shown parameter exceeds twice the inter-ensemble standard deviation of the historical period 1981–2009, hatch- ing is applied to the areal plot. This is also reflected in the more robust decreases found in SWH in Fig. 4 for these sea- sons. It should be noted that an ensemble of three members is small and a larger ensemble would be preferable in order to estimate a robust inter-ensemble spread (the hatching). How- ever, the computational resources available to run the wave
Everyone agrees with physical law which shows that Coriolis force acts to the right in the northern hemisphere (to the left in the southern hemisphere). Therefore, Hadley, Ferrel and Polar cells trigger due to earth’s rotation effects: easterly (or westerly) winds depending on location where these cells are observed. Prevailing surface winds that result are distributed as suggested by the Figures 4-6. Figure 4(a) and Figure 4(b) suggest the pro- files of prevailing surface winds during the summer (with respect to the northern hemisphere). Figure 5(a) and Figure 5(b) suggest the profiles of prevailing surface winds when the ITCZ is aligned with the geographic equator. Figure 6(a) and Figure 6(b) suggest the profiles of prevailing surface winds during the winter (with respect to the northern hemisphere). The eT-diagram coordinates (6.11 mb and 0.0098˚C) of water triple-point allow each of us to easily locate Polar front and Horse latitude respectively. The general circulation plots provided by Figure 4(a) and Figure (6a) clearly show that: prevailing winds (easterlies or westerlies) at 500 hPa levels (levels commonly used in meteorology as reference) can be totally different with ground surface prevailing winds. Unfortunately meteorologists confused all the time, prevailing winds at 500 hPa with those on the ground surface. e.g., In January the TV meteorological presenters see the arrival of tropical warm air masses on Eu- rope. In fact, this is exactly cold tropical air masses that descend along the 0.0098˚C isotherm Figure 6(a) and contribute to the installation of winter in Europe. i.e., Ferrel and Hadley cells carry (through isothermal com- pression) cold air masses from tropical mid-troposphere to European low-troposphere (Figure 6(a)). This mi- sinterpretation is due to the fact that the general circulation was long remained poorly drawn and contradictions like this one would be completely avoided when our appropriate and faithful plots of the general circulation will be delivered to a very wide audience.
The effect of coupling on model predictions becomes more important (Janssen et al., 2004) with increasing grid resolu- tion, which therefore emphasises the need for coupling on the regional scales. Spatial and temporal changes in the wave and wave energy propagation are not yet sufficiently addressed in high-resolution regional atmospheric models. The shal- low water terms in the wave equations (depth and current refraction, bottom friction and wave breaking) play a domi- nant role near coastal areas, especially during storm events, where the wave breaking term prevents unrealistically high waves near the coast. The spray caused by breaking waves modulates the atmosphere boundary layer. Air–sea interac- tion is also of great importance in regional climate mod- elling. Rutgersson et al. (2010, 2012) introduced two dif- ferent parameterisations in a European climate model. One parameterisation uses roughness length and includes only the effect of a growing sea, as proposed by Janssen (1991). The other uses wave age and introduced the reduction of roughness due to swell. In both cases, these parametrisa- tions affected the long-term averages of atmospheric param- eters notably and demonstrated that the swell has an im- portant impact on mixing in the boundary layer. Järvenoja and Tuomi (2002) emphasised the necessity to use wind data with fine temporal discretisation in the wave model in the Baltic Sea and found that the impact of the coupled model on the meteorological part of the model can mainly be seen in predicted surface winds. For the Mediterranean
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Abstract. In the Southern Hemisphere, macroscale atmo- spheric systems such as westerly winds and the southeast Pacific subtropical anti-cyclone (SPSA) influence the wind regime of the eastern austral Pacific Ocean. The average and seasonal behaviors of these systems are well known, although wind variability at different time and distance scales remains largely unexamined. Therefore, the main goal of this study was to determine the variabilities of surface winds on a spatiotemporal scale from 40 to 56 ◦ S, using QuikSCAT, Advanced Scatterometer (ASCAT), and the fifth major global European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5) surface-wind infor- mation complemented with in situ meteorological data. In addition, interactions between the atmospheric systems, to- gether with the ocean–atmosphere response, were evaluated for the period 1999–2018. The empirical orthogonal func- tion detected dominance at the synoptic scale in mode 1, rep- resenting approximately 30 % of the total variance. In this mode, low and high atmospheric pressure systems character- ized wind variability for a 16.5 d cycle. Initially, mode 2 – which represents approximately 22 % of the variance – was represented by winds from the west/east (43–56 ◦ S), occur-
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SST-dependent SLW variability can help analyze the coupled climate dynamics of the Southern Ocean, especially when combined with oceanic General Circulation Models (GCMs). Climate variability over the Southern Ocean is likely to be of global significance, due to this ocean’s special role in linking the Atlantic, Pacific, and Indian basins. However, progress in understanding the dynamics of large-scale air- sea coupling over the Southern Ocean has been slow, largely due to the very low density of in situ measurements in this region. Recently launched NASA satellites provide accurate high-resolution global measurements of important climatic variables such as SST and SLW. These global fields now per- mit the construction of empirical air-sea interaction models for the Southern Ocean. Despite improved data coverage in the region, estimating the propagator of the above mentioned statistical models remains an ambitious and challenging task, since (1) there are still missing data due to the presence of strong winds or heavy rains, and (2) such a model has to have an unprecedentedly large number of degrees of free- dom, due to high-dimensional nature of global-scale air-sea interaction. The model construction thus requires major al- gorithmic revisions and gap-free datasets, which we develop and describe in detail below.
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Within the PBL, agreement between unfiltered surface AMOG winds and AJAX winds was poor, unsurprising be- cause surface winds are strongly affected by obstacles. How- ever, by filtering AMOG winds (collected 3 m above the sur- face) for the strongest 5 %, agreement was within 15–20 % for the along-slope – i.e., north – winds and better for up- slope winds (west). Specific exceptions were when AMOG was in a dense grove of pines, and when AJAX flew behind into the lee of a mountain peak. Surface winds are modulated by a wide range of surface factors, including trees, steep hills and hillocks, steep slopes, rolling hills, and structures (Sup- plement Fig. S5). However, a combination of gusts (among thin wooded terrain on steep slopes) and the limited spatial extent of most obstacles underlies the agreement between the filtered AMOG and AJAX wind profiles. Agreement is better for the upper portions of the PBL (within 10–20 %), where Sierra Nevada slopes are steeper. In contrast, the slope lower in the PBL is gentle, and surface boundary layer effects are more pronounced, biasing wind speeds slower.
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Although this physical model incorporates important information provided by the observed central pressure and the location of the eye of the storm there are known deficiencies in this formulation. For example, the Holland winds are symmetric around the storm center, however it is known that wind speeds are typically higher on the right hand side of the hurricane (with respect to the storm movement). Various adaptations of this physical model have been proposed. Xie et al. (2006) create an asymmetric model by incorporating data provided by the National Hurricane Center (NHC) guidance on the maximum radial extent of winds of a given threshold in the four quadrants of the storm. The NHC uses a parametric wind formula similar to the Holland model to force the ocean model. Another approach would be to use a coupled atmospheric-oceanic numerical model to simulate the surface winds at the boundary layer of the ocean model. Global mesoscale numerical weather forecasting models such as the Mesoscale Model (MM5) and the Weather Research and Forecasting Model (WRF) are capable of making forecasts for surface level wind fields. However these forecasts are not suitable for real-time forecasts of hurricane winds. Historically these models have been unable to accurately reproduce the intensity of hurricane force winds. More recent reports show that MM5 and WRF are able to obtain more realistic wind speeds when run at high resolution (on the order of 1 km grid size). However the CPU time required to produce these modeled winds prevents such model runs from being used in real-time applications. The best approach for regional storm surge forecasting is still using parametric hurricane models, as discussed in Xie et al. (2006). For further details on storm surge forecasting for the coastal ocean and estuarine systems see Xie et al. (2004), Peng et al. (2004), and references therein.
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Wind flow over a flat surface can be described using a logarithmic velocity profile (Ellis and Sherman, 2013). As flow approaches an obstacle, such as a dune, changes in pressure cause wind flow to alter in both speed and direction depending on the topography of the obstacle. In general wind flow becomes accelerated as streamlines compress with height over the windward slope, though this process is seldom linear (Lancaster, 1994; p 484). In the lee of an obstacle, wind speed slows, streamlines expand and flow separation may occur (Walker and Nickling, 2002). As complex near surface winds drive sediment transport, considerable research has been devoted to boundary layer dynamics over dune and blowout topographies in an effort to understand erosion, deposition and sediment transport pathways. This research has been performed in the field (Hesp and Hyde, 1996; Walker, 1999; Baddock et al. 2011; Delgado-Fernandez et al. 2013), in wind tunnels (Walker and Nickling, 2003; Wiggs et al. 1996) and by numerical modelling (Parsons et al. 2004a; Zhang et al. 2012; Araújo et al. 2013). The majority of recent numerical modelling has been implemented by Computational Fluid Dynamic (CFD) simulations of flow.
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Abstract. Due to the stability of the boundary-layer stratification, high-latitude winds over complex terrain are strongly affected by blocking and channelling effects. Con- sequently, at many low-lying communities in the Canadian Archipelago, including Cape Dorset and Iqaluit considered in this study, surface winds for the most part are from two di- ametrically opposed directions, following the orientation of the elevated terrain. Shifts between the two prevailing wind directions can be sudden and are associated with geostrophic wind directions within a well defined narrow range. To quan- titatively investigate the role of large-scale pressure gradi- ents and the quasi-geostrophic overlying flow, an idealised dynamical system for the evolution of channelled surface winds is derived from the basic equations of motion, in which stability of stationary along-channel wind directions is de- scribed as a function of the geostrophic wind. In comparison with long-term horizontal wind statistics at the two locations it is shown that the climatologically prevailing wind direc- tions can be identified as stationary states of the idealised wind model, and that shifts between prevailing wind direc- tions can be represented as stability transitions between these stationary states. In that sense, the prevailing local wind con- ditions can be interpreted as attracting states of the actual flow, with observed surface winds adjusting to a new stable direction as determined by the idealised system within 3– 9 h. Over these time-scales and longer it is therefore advan- tageous to determine the relatively slow evolution of the ob- servationally well-resolved large-scale pressure distribution, instead of modelling highly variable surface winds directly. The simplified model also offers a tool for dynamical down- scaling of global climate simulations, and for determining future scenarios for local prevailing wind conditions. In par- ticular, it allows an estimation of the sensitivity of local low- level winds to changes in the large-scale atmospheric circu- lation.
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McCaul and Weisman (2001) showed through supercell simulations that storm strength is influenced by the vertical distribution of CAPE, with higher concentrations of CAPE in the low levels corresponding to more intense convection. In a more recent study, Lane (2008) also indicated that the vertical distribution of CAPE may be a factor in determining likelihood of tornadogenesis in HSLC cases. McCaul (1991) found similar trends in tropical low CAPE environments, as static stability below 700 hPa (approximately 3 km) was weak in tornado events compared to Oklahoma supercell composite soundings. Additionally, though precipitable water values are comparable between high and low CAPE severe events (Guyer and Dean 2010), many previous studies (e.g., Schneider et al. 2006; Schneider and Sharp 2007; Guyer and Dean 2010) indicate the prevalence of low LCLs and high low-level moisture content in low CAPE tornado environments. Furthermore, severe HSLC environments (particularly those associated with tornadoes or damaging winds) are often characterized by substantial curvature in low-level winds, leading to large values of storm- relative helicity (SRH), which is generally proportional to the amount of streamwise vorticity that can be ingested by a supercell (e.g., Cope 2004; Wasula et al. 2008; Guyer and Dean 2010; Latimer and Kula 2010). Even in low CAPE environments that are marginally unstable throughout the depth of the troposphere, dynamically-driven strong updrafts near the surface may provide maximum potential for tilting and stretching of this environmental SRH along with storm-generated vertical vorticity, leading to the development of mesocyclones, mesovortices, and/or tornadoes.
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MISR paths can also be paired with GOES full-disk and MESO scenes. The refresh period for MESO scenes can be either one minute or 30 seconds and generally 15 minutes for a full disk. The rapid refresh for a MESO scene is an interesting capability for following highly dynamic meteorological phenomena such as hurricanes. With the more rapid refresh, there is greater assurance that one of the fundamental assumptions of AMVs, that a tracked feature remains invariant and only translates, is valid. This is of course traded off against the shorter time between refresh cycles, so whatever uncertainty exists in subpixel measurements of feature displacement will have greater effect on the uncertainty in retrieved wind velocity. We have yet to fully assess the implications of this tradeoff on the 3D-Winds algorithm, but we have experimented with 3D-Wind retrievals using MISR and GOES MESO 30-second imagery. Figure 13a is an example from Hurricane Florence with a very high density of successful retrievals. We can clearly see the low- altitude cyclonic winds to the south of the eye that feed the storm with warm moist air from boundary layer and high-altitude anticyclonic winds to the north carrying away cooler air.
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Another source of error in the wind-driven measurements reported here is the possible evolution of the regional circula- tion during the time gap between the observation of the pre- existing regional circulation and the velocity deconstruction during the high wind period. Although difficult to quantify given the available data, the regional circulation is evolving, to a certain extent, during the time gap between the obser- vation of the regional circulation and the analysis periods of high winds. In addition, the regional circulation could begin to be modified through interactions with the large wind- and wave-induced currents. The extent to which this is occurring can be seen qualitatively in Figs. 7 and 8 by comparing the change of the SST fields in time. One example of this is ev- ident in the southeastward progression of the front seen in the SST fields associated with the January high wind event. The movement of this front seems to generally mimic the movement of the drifters during this high wind event shown in the top panels of Fig. 10. The overall timescales on which these flow features evolve or are altered by each high wind event are beyond the scope of this paper, given the available dataset. However, the relatively short timescales of these pe- riods of high winds and the overall agreement between the pre-existing circulation estimates and the SST fields (shown in Figs. 7 and 8) suggest that the method of velocity decon- struction used here is adequate. In addition, the decreased variability seen in the deflection angles after the subtraction of the pre-existing regional circulation (Fig. 11) suggests, a posteriori, that the regional circulation does maintain its structure to a reasonable extent during the periods of increas- ing winds being analyzed.
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Pressure differentials, leading to expansion or contraction of air within soil pores, leading to greater and more rapid mixing within pores, have been proposed as a mechanism by which air may be mixed between soil pore spaces and the overlying atmosphere (i.e. “pressure pumping”) (Den- mead, 1979; Yonemura et al., 2000; Takle et al., 2004; Xu et al., 2006; Flechard et al., 2007; Reicosky et al., 2008; Rey et al., 2012). However, the effects of high external winds within the linear wind tunnel on trace gas fluxes from low or moderate wind environments within the isolated toroid (the isolated toroid in this scenario is similar to the real- world scenarios of (i) a forest verge, nearby an open field, (ii) an open field surrounding a slight depression with deeper grass depth providing a protected canopy, or (iii) a hedgerow) cannot be explained through this pressure-pumping model. While pressure waves have been demonstrated to travel up to 50 cm within soils (Takle et al., 2004; Flechard et al., 2007; Reicosky et al., 2008), such waves, under high ex- ternal wind conditions, would lead to lower relative pres- sure in the soil below the toroid. A lower pressure below the toroid would lead to a reduction in, or neutral impact, on fluxes of CO 2 (similar concentrations, but lower pressure, in
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tical velocity spectra should be further studied. So far, how- ever, only a few studies have been carried out focusing on the frequency spectra of vertical velocities. The pioneer work using radar may go back to Röttger (1981) who analyzed the SOUSY-VHF-Radar observations. Ecklund et al. (1986) gave a preliminary climatology of the frequency spectra of verti- cal velocity derived by analyzing the data taken from radars at four widely separated geographical locations. Under quiet conditions with weak winds (< 10 m s −1 ), their spectra show a nearly constant slope close to 0 at periods larger than the Brunt–Vaisälä period τ B , a bump (i.e., excess energy) at τ B ,
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These m odels d em o n strate th a t th e velocity stru c tu re of th e cool wind has a strong effect on th e R am an Unes, b o th in term s of th e intensity profile and th e polarization stru c tu re . T he smaUest differences betw een th e const ant- velo city wind and th e accelerating wind m odel occurs a t th e larger binary separations, particu larly for winds w ith a high m ass-loss ra te . This is presum ably because the Une form ation in th e accelerating m odel is occuring in a region where th e wind is close to its term inal velocity and Uttle scatterin g is occuring in th e steeply accelerating region of th e wind. M odel ( 6) shows th a t th e veloc ity law m ay be im p o rta n t lower mass-loss ra te m odels, even a t wide binary separations, since th e scatterin g volume encom passes th e accelerating region of th e wind, giving rise to changes in polarization stru c tu re in th e blue Une-wing (where th e sc atterin g is occuring in th e p a rt of th e wind approaching th e p aren t photon source). Clearly th e velocity s tru c tu re becom es m ost im p o rta n t when th e binary separation is smaU, and th e Une fo rm atio n is occuring in a region of th e wind w ith large velocity gradients. M odel (c) shows th a t th e R am an Une intensity is stronger in th e accelerating wind m odel, m ainly because th e wind density is m uch higher a t smaUer separations th a n in th e co n stan t velocity m odel.
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The use of accelerometers on satellites to measure thermo- spheric winds had rarely been reported in the literature un- til recently (Marcos and Forbes, 1985; Forbes et al., 1993); however, over the last few years challenging mini-satellite payload (CHAMP) and GOCE winds have been reported (e.g. Förster et al., 2008; Doornbos et al., 2010). The ad- vantage of this technique consists in the fairly direct in situ measurement, with relatively high spatial (temporal) reso- lution, of the cross-track wind component along the orbital track with only a limited number of special assumptions re- garding the data interpretation. Adding more satellites (e.g. GRACE and GRACE-FO), should allow better full wind vec- tor reconstructions in terms of statistical averages (Förster et al., 2008, 2017) as well as parameterized statistical studies of the upper thermosphere dynamics in the near future. As a re- sult, it makes it imperative that the derived winds are correct because satellites provide global coverage of the upper atmo- sphere, unlike the small number of ground-based instruments currently in existence. The larger databases and global cover- age of the satellites will particularly influence semi-empirical models such as the Horizontal Wind Model (HWM; Drob et al., 2015), which is commonly used as a climatology of winds to provide initial boundary conditions and validation for physics-based global circulation models (GCMs).
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Figure 1 shows the radial magnetic field configuration at the surface of τ Boo from observations made by Rim Fares and collaborators  in July 2008 using the Naval echelle spectropolarimeter on the Telescope Bernard Lyot (TBL) in France. This magnetic field map differs slightly from the those published of the same data set previously due to advances in the ZDI method. The map shows multiple regions of positive and negative magnetic field, with several large positive field spots and a large negative spot around the Northern pole. The field is less detailed at the Southern pole due to a lack of information resulting from the inclination angle of the star with respect to us. The black line indicates where the field is zero.
Lord Kelvin (1886) showed that water flowing over an obstacle will be depreE::sed at the surf ace, in the same way as the positive pressure used by Lord Rayleigh, hence the resulting wave pattern should be of the same form . However , when Lord Kelvin, after observing the waves in the wake of a boat, derived expressions for the surface of a channel, he found waves downstream only with a fast exponential decay on the upstream side of the obstacle . His initial solution contains waves on both sides of the obstacle but he imposes a condition of no waves upstream to obtain what he calls
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Optimal conditions for retrieving sea ice topography and movement are given when two satellites fly as a tandem in close formation (“single-pass InSAR”). The opportunity to study the potential of single-pass satellite InSAR for map- ping of sea ice topography arose during the TanDEM-X Sci- ence Phase, which started in September 2014 and lasted for 17 months (Maurer et al., 2016). The TanDEM-X mission (TerraSAR-X add-on for Digital Elevation Measurements) has primarily been designed for topographic mapping of the Earth’s land masses (Krieger et al., 2007). In standard opera- tion mode the achievable relative accuracies are 2–4 m verti- cally (dependent on slope of terrain and land cover type) and 3 m horizontally at a horizontal sampling of 12 m (Krieger et al., 2007). This mode is optimized for topographic mapping of the land surface but is not sufficient for retrieving height variations of the sea ice surface. The Science Phase was ini- tiated to demonstrate new products and applications such as digital elevation models with higher accuracies than in stan- dard mode or measurements of ocean currents. It consisted of different sub-phases, among them a large cross-track base- line formation with mean along-track separation of zero that was initiated in March 2015. Data takes were performed in a bistatic mode (see below). The comparatively large base- lines in this phase translated to a very high sensitivity for object elevations on the order of decimeters. The data that are presented in this paper were acquired during the large cross-track baseline formation.
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scape, but one that is always changing and transforming. Roads thus always have a flow and roads are always in the making. Tim Ingold’s statement on the importance and result of these interactions that carry “transformative potential” is very suitable here and pinpoints the process in which “beings of all kinds, more or less person-like or thing-like, continually and reciprocally bring one another into existence” (Ingold 2011: 68). It is the relation between roadlessness, the snow covered surface and the trucker that makes a road. without actions on the driver’s part or the conditions that roadless- ness presents a road would not happen and would not emerge. Likewise, through this very experience or coping with difficult roads and states of roadlessness a novice learns skills and becomes a driver, and an experienced dalnoboishchik sharpens his skills. “one is not born a driver, one becomes a driver”; the often heard phrase succinctly underlines the transformative effect of roadlessness and roads on drivers.
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