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Landon Rogers
Landon Rogers

RAW Power 3.4


Any way you use it, you get the same impressive editing features. With either app mode, you can rate, flag, and filter too.RAW Power is great with JPEGs, with powerful adjustments absent from Apple's Photos (such as LUTs, Depth Effect and Chromatic Aberration)If you loved Aperture's advanced RAW processing, you'll feel at home with RAW Power. Adjustments like Boost, previously only available in Aperture, help you improve your images in ways that simply can't be done in any other application.OVERVIEW




RAW Power 3.4



RAW Power unlocks the power of Apple's RAW engine with simple, easy-to-use controls. Use it as an editing extension inside Apple Photos, or as a standalone, non-destructive RAW photo editor. If you loved Aperture's advanced RAW processing adjustments, you'll feel right at home with RAW Power. Using adjustments like Boost, previously only available in Aperture, you can improve your images in ways that simply can't be done in any other application. RAW Power takes advantage of RAW processing improvements in macOS Sierra.


The Federal Aviation Administration (FAA) does not allow any type of battery onboard an aircraft that exceeds 27,000 mAh. Keep these guidelines in mind before flying and be sure to properly pack your power banks!


Fully automatic and awe inspiring, the Ultratech OTF from Microtech sets the bar for out-the-front knives. Whether you are deploying your blade or bringing your blade back to its resting position, you will be amazed at the consistency of its firing mechanism. This dual action automatic by Microtech embraces quality, raw power and craftsmanship.


Fully automatic and awe inspiring, the Ultratech OTF from Microtech sets the bar for out-the-front knives. Whether you are deploying your blade or bringing your blade back to its resting position, you will be amazed at the consistency of its firing mechanism. This dual action automatic by Microtech embraces quality, raw power and craftsmanship.


With the increased cubic capacity from the 94mm stroker crank, increases of up to 50% in torque and low to mid range power are achievable. Boost will come on earlier in the rev range and peak power will increase.


Garden of Life Raw Probiotics Kids is a Raw, Certified USDA Organic, whole food probiotic formula specifically designed to meet the unique needs of children. Formulated with powerful probiotics, 23 Raw and organic fruits and vegetables, plus Raw inulin, a prebiotic that supports probiotic growth, Raw Probiotics Kids delivers over 5 billion live probiotic cells per daily serving. Raw means our probiotics are uncooked, untreated, unadulterated; with no binders or fillers and no carriers


Wind power, defined as the work imparted to the ocean by the winds, provides the dominant source of APE variability in the tropical Pacific through the conversion of kinetic energy into APE. Changes in wind power force changes in APE and, by proxy, Niño SST on seasonal time scales, such that positive wind power anomalies correspond to cooling SSTs and negative wind power anomalies correspond to warming. Given this association, wind power has thus been identified as another potential precursor of ENSO variability by Goddard and Philander (2000) and Philander and Fedorov (2003). While the role of wind power in an ENSO energetics framework has been explored by numerous studies focusing primarily on climate model data (e.g., Brown and Fedorov 2008; Brown and Fedorov 2010; Hu et al. 2014), there is limited assessment of its actual viability as an ENSO predictor.


The predominant interest of this study is to gain insight into the performance of the adjusted wind power as a predictor in the observed world. However, the observational record is only 23 years, subsequently limiting the sample size for a seasonally based forecast assessment each year. To examine the sensitivity of predictor skill to sample size we analyze the 1800 years of coupled model data from the CESM LENS preindustrial (PI) control run (Kay et al. 2015). This analysis in turn will be used to better contextualize the observational analysis in the following section. The use of the PI control is itself not without caveats, the first being that the control run is not an operational forecast model, and therefore cannot verified against observations on an event-by-event basis. A second caveat is that CESM, as with other coupled models, is subject to the well-known cold tongue and double ITCZ biases, which impact the estimation of wind power through the propagation of these biases to the tropical wind stress and zonal surface currents used to compute wind power (Li and Xie 2014; Burls et al. 2016; KB19). Specifically, KB19 find that these climatological biases impact the efficacy of the wind power adjustments in LENS compared to observations (an important aspect to keep in mind when evaluating the skill of adjusted versus unadjusted wind power within the LENS dataset). Despite these limitations, CESM is generally regarded as a good simulator of ENSO variability (Gent et al. 2011; Hu and Fedorov 2017) and provides a long independent data sample to test the general behavior of the predictive models.


In general, the unadjusted wind power (WPu) has the lowest RMSE for forecasts trained on JFM and FMA data over all other predictors. From AMJ through JJA, integrated wind stress takes over, after which SST becomes the best predictor. MAM is the most sensitive time of the year, when WPa, WPu, and wind stress are close enough in value that even a small change can impact the comparative error.


For comparison, the random walk skill test from section 4 is applied to the observations, using a climatological forecast as a reference (Fig. 7) and with the consideration that such a metric is more effective for much larger samples. Certain predictors quickly show indications of skill, for example WPu in FMA (Fig. 7b) and wind stress in MAM (Fig. 7c), though again, the sample size hinders the conclusiveness of the results. Interestingly, while the NMME outperforms both adjusted and unadjusted wind power in the AMJ and JJA seasons in terms of RMSE and correlation (Fig. 6), it does not meet the random walk climatological forecast skill criterion at those leads. A closer analysis of the actual forecast values shows that NMME does not have large outlier forecasts but is also rarely the most accurate forecast among the competing models. In contrast, the regression models are generally more accurate for each forecast comparison but have a few outlier years in which their errors are large enough to inflate their RMSE. This illustrates how the random walk and RMSE provide different insights into the forecast behavior.


One of the highlighted advantages of the adjusted wind power framework is its ability to associate the direction of wind bursts with the correct dynamical SST response in the Niño-3.4 index (KB19). This is because WPa removes ambiguity in the wind power signal resulting from an EWE versus a WWE (KB19). This is particularly important during El Niño events where strong WWEs play a major role in the event development. The ability of adjusted wind power to more effectively capture the contributions of wind bursts motivates a brief analysis of adjusted wind power that is specifically associated with wind bursts.


While the LENS analysis points to several difficulties with an observational predictability assessment due to the shortness of the observational record, it also provides useful context for inferences drawn from observed results. While uncertainty is high, the differences from the LENS climate versus reality still manifest in the structure of the RMSE, correlations, and random walk scores. Even in the observationally consistent LENS samples, the wind power correlations are larger at the longest lead times compared to observations. When comparing the RMSE for the observations versus the short LENS samples, the observed errors for JFM and FMA are much higher (Fig. 1, left). The random walk test likewise shows that both wind power indices have skill in JFM and FMA in LENS but not observations.


Overall, the analysis here highlights the many difficulties of ENSO forecasting and, unsurprisingly, the empirical adjusted framework does not appear to be the magic bullet. However, even considering the differences between the LENS data and reality, the adjusted framework shows its advantages relative to the unadjusted wind power in both observations and state-of-the-art climate models. The adjusted wind power holds up comparably to the other predictors and demonstrates that correctly accounting for the wind bursts reduces error when representing ENSO.


Power-to-weight ratio is one of the key metrics that determines how quickly a cyclist can climb uphill.\nOf course, the total power output you can produce plays a role in determining your overall cycling performance, but raw wattage alone doesn\u2019t always tell the entire story, especially when climbing hills comes into the equation.\nThis is where your power-to-weight ratio \u2013 expressed as watts-per-kilogram \u2013 is key.\nNormalising your power output to your body weight can provide a better means of assessing your ability over hilly terrain and comparing your performance to other cyclists.\nHere, we\u2019ll look at how to calculate your own power-to-weight ratio, how to improve it and what effect that improvement can have on your cycling performance.\nCalculating power-to-weight ratio\nYour power-to-weight ratio can be calculated as watts (W) divided by your body weight in kilograms (kg), expressed as W\/kg.\nAll you need to do is take the power output you can sustain for a given duration or at a certain physiological threshold (we\u2019ll come on to that) and divide it by your body weight in kilograms to find your own W\/kg.\nThe watts used in this calculation will most commonly refer to your power output at your maximal steady-state power, which could be an identified lactate concentration (e.g. 4 mmol\/L), critical power or, most commonly, Functional Threshold Power (FTP).\nFor example, if you complete an FTP test and your Functional Threshold Power is determined to be 250 watts, and you weigh 75kg, your power-to-weight ratio here will be 3.33 W\/kg.\n\n If you\u2019re riding with a power meter, the latest bike computers can be set up to display W\/kg. Simon Bromley \/ Immediate Media\nWhile lactate concentration, critical power and FTP\u00a0are slightly different to one another, they are all essentially trying to identify a maximal point at which exercise intensity can be maintained without the rapid onset of fatigue.\nAt the same time, your W\/kg over shorter and longer durations can just as easily be calculated to see how you stack up in other types of effort \u2013 for example, on a single hill or over the course of an entire event or ride.\nOther common durations of comparison include your W\/kg over one minute (roughly indicating your anaerobic capacity) and five minutes (roughly indicating your aerobic capacity, or VO2 max).\nWhy does W\/kg matter?\n\n Power-to-weight ratio is particularly important when climbing. Russell Burton \/ Immediate Media\nAs mentioned above, your power-to-weight ratio really starts to affect performance when climbing comes into the picture.\nThat\u2019s because, when climbing, the dominant source of resistance to your forward motion is gravity.\nThe gravitational force you have to overcome is dependent on your body weight. So producing 300W as a 60kg person (5 W\/kg) will get you up the hill faster than producing 300W as a 90kg person (3.3 W\/kg).\nIn contrast, when riding on the flat, the dominant source of resistance to your forward motion is air resistance. This increases with speed and also depends on your frontal area (i.e. how big you are).\nIn this case, body weight has a small indirect impact, because bigger people are often heavier. But this impact is much less than for climbing and, ultimately, it\u2019s usually raw power that makes the difference on the flat rather than W\/kg.\n\n The growing popularity of smart trainers has made training with power, and thus power-to-weight ratio, more accessible. Simon von Bromley \/ Immediate Media\nA cyclist\u2019s power-to-weight ratio isn\u2019t just a relevant metric for riders who want to climb faster in the real world.\nThe virtual roads and trails found on indoor cycling apps, such as Zwift, RGT Cycling and Rouvy, use your W\/kg as a major determinant in how fast you go, and thus having a good W\/kg can help you win Zwift races and stay up at the front in virtual group rides on hilly courses.\nZwift also uses W\/kg to categorise races and group rides, so you can find a group to match your ability.\nWhat\u2019s a good power-to-weight ratio?\n\n Professional cyclists can have an FTP with a power-to-weight ratio north of 6 W\/kg. Craig Zadoroznyj \/ SWPix.com\nNow that you have some idea about how your power-to-weight can affect performance, what do good numbers actually look like for different ability levels?\nA power profile chart is commonly used to compare different ability categories in terms of power-to-weight ratio.\nPower-to-weight ratio chart\n\n \ufeffMaleFemale5-sec1-min5-minFTP5-sec1-min5-minFTP World class25.211.57.66.619.49.36.75.7 24.911.47.56.519.29.26.65.6 24.611.37.46.4199.16.55.5 24.311.17.36.318.896.45.4 24117.26.218.58.96.45.4 23.710.97.16.118.38.86.35.3 23.410.87618.18.76.25.2 23.110.76.95.917.98.66.15.1 Exceptional22.810.66.85.817.78.565 22.510.46.65.717.48.55.94.9 22.210.36.55.617.28.45.84.9 21.910.26.45.6178.35.74.8 21.510.16.35.516.88.25.64.7 21.2106.25.416.68.15.54.6 Excellent20.99.96.15.316.385.44.5 20.69.765.216.17.95.34.4 20.39.65.95.115.97.85.24.3 209.55.8515.77.75.14.3 19.79.45.74.915.57.654.2 19.49.35.64.815.27.54.94.1 Very good19.19.25.54.7157.44.84 18.895.44.614.87.34.73.9 18.58.95.34.514.67.34.63.8 18.28.85.24.414.47.24.53.8 17.98.75.14.314.17.14.43.7 17.68.654.213.974.33.6 Good17.38.44.94.113.76.94.23.5 178.34.7413.56.84.13.4 16.78.24.63.913.36.743.3 16.48.14.53.8136.63.93.3 16.184.43.712.86.53.83.2 15.87.94.33.612.66.43.73.1 Moderate15.57.74.23.612.46.33.63 15.27.64.13.512.26.23.52.9 14.97.543.411.96.13.42.8 14.67.43.93.311.763.32.8 14.37.33.83.211.563.22.7 147.23.73.111.35.93.12.6 Fair13.773.6311.15.832.5 13.46.93.52.910.85.72.92.4 13.16.83.42.810.65.62.82.3 12.86.73.32.710.45.52.72.2 12.56.63.22.610.25.42.62.1 12.26.53.12.5105.32.62.1 Novice 211.96.332.49.75.22.52 11.66.22.82.39.55.12.41.9 11.36.12.72.29.352.31.8 1162.62.19.14.92.21.7 10.75.92.528.84.82.11.7 10.45.72.41.98.64.821.6 Novice 1105.62.31.88.44.71.91.5 9.75.52.21.78.24.61.81.4 9.45.42.11.684.51.71.3 9.15.321.67.74.41.61.2 8.85.21.91.57.54.31.51.2 8.551.81.47.34.21.41.1 8.24.91.71.37.14.11.31 \n\n(Values shown are in W\/kg. Adapted from Allen & Cooper et al. 2010)\nYour W\/kg across the different durations can give some insights into where you are most\/least naturally talented or where you need to improve to achieve your goals. You can then use this information to create a training plan.\nHowever, it\u2019s worth noting that you\u2019re unlikely to excel across all durations.\nAn FTP of more than 6 W\/kg is commonly associated with professional cyclists who specialise in climbing. However, these cyclists often have weaker 5-sec and 1-min W\/kg values compared to sprinters, due to their muscles being heavily adapted to aerobic metabolism.\nSimilarly, these sprinters will have lower W\/kg ratios at their FTP due to factors such as\u00a0greater muscle mass and larger anaerobic capacities.\nUltimately, a W\/kg chart like this can give you a good idea of how you stack up against other cyclists, what durations you\u2019re most suited to and where you might want to focus your training.\nHow to improve your power-to-weight ratio\n\n You can improve your power-to-weight ratio by increasing your power output, lowering your body weight, or both. Zwift\nWhen looking to improve your power-to-weight ratio, perhaps with the goal of becoming a faster climber on the bike, this can be achieved by increasing your power output, lowering your body weight or ideally accomplishing both at the same time.\nReducing your body weight is ideally achieved by reducing body fat (rather than muscle), which can be done by ensuring you have a small energy deficit between what you eat and how much you burn, and following a diet that allows you to do this over the long-term.\nIn general, diets that help achieve this are relatively high in protein (e.g. between 1 to 1.5g\/kg of body weight), fruit, vegetables and wholegrains.\nThese foods are filling but also relatively light in calories, and the moderately high protein intake can also help conserve your muscle mass during weight loss.\nIf you\u2019re training quite a lot, you\u2019ll also want to make sure you\u2019re getting in a good proportion of carbohydrates in order to fuel high-intensity workouts and support your immune system (between 6 to 10g\/kg of bodyweight depending on training volume).\nTraining to improve power output will differ depending on the duration you\u2019re targeting (e.g. W\/kg over one minute versus W\/kg over one hour). However, many cyclists aim to improve their W\/kg at their maximal steady-state power (e.g. FTP).\nTraining to improve your power at your maximum steady-state is best achieved through:\nLong, low-intensity base training rides at around 55 to 75 per cent FTP or 68 to 83 per cent threshold heart rate (useful if you\u2019re using training zones), which helps reduce lactate production and improve lactate clearance.\nIntervals at around your threshold power or heart rate, which help improve your ability to clear lactate. We particularly like intervals that alternate between intensities just above and just below your threshold.\nIntervals that allow you to elevate your heart rate close to maximum and hold it there for several minutes, helping to develop your aerobic capacity, which impacts how much lactate you produce and clear. We particularly like intervals that start hard (e.g. 120 to 130 per cent FTP or 8 to 9\/10 effort) for a few minutes. Then you can adjust your intensity to whatever is required to hold your heart rate above 90 per cent max for a further four to six minutes.\n","image":"@type":"ImageObject","url":"https:\/\/images.immediate.co.uk\/production\/volatile\/sites\/21\/2021\/02\/Climbing-on-a-road-bike1-bf2ffba.jpg?quality=90&resize=768,574","width":768,"height":574,"headline":"Power-to-weight ratio explained: why W\/kg is important and how to improve yours","author":["@type":"Person","name":"Tom Bell"],"publisher":"@type":"Organization","name":"BikeRadar","url":"https:\/\/www.bikeradar.com","logo":"@type":"ImageObject","url":"https:\/\/images.immediate.co.uk\/production\/volatile\/sites\/21\/2019\/03\/cropped-White-Orange-da60b0b-04d8ff9.png?quality=90&resize=265,53","width":182,"height":60,"speakable":"@type":"SpeakableSpecification","xpath":["\/html\/head\/title","\/html\/head\/meta[@name='description']\/@content"],"url":"https:\/\/www.bikeradar.com\/advice\/fitness-and-training\/power-to-weight-ratio\/","datePublished":"2022-03-08T15:00:00+00:00","dateModified":"2022-03-08T15:00:25+00:00"}] Power-to-weight ratio explained: why W/kg is important and how to improve yours Power-to-weight ratio is one of the most important indicators of climbing performance. Here's everything you need to know 041b061a72


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