Despite an intuitive relationship between technique and both running economy (RE) and performance, and the diverse techniques used by runners to achieve forward locomotion, the objective importance of overall technique and the key components therein remain to be elucidated.PurposeThis study aimed to determine the relationship between individual and combined kinematic measures of technique with both RE and performance.MethodsNinety-seven endurance runners (47 females) of diverse competitive standards performed a discontinuous protocol of incremental treadmill running (4-min stages, 1-km·h−1 increments). Measurements included three-dimensional full-body kinematics, respiratory gases to determine energy cost, and velocity of lactate turn point. Five categories of kinematic measures (vertical oscillation, braking, posture, stride parameters, and lower limb angles) and locomotory energy cost (LEc) were averaged across 10–12 km·h−1 (the highest common velocity < velocity of lactate turn point). Performance was measured as season's best (SB) time converted to a sex-specific z-score.ResultsNumerous kinematic variables were correlated with RE and performance (LEc, 19 variables; SB time, 11 variables). Regression analysis found three variables (pelvis vertical oscillation during ground contact normalized to height, minimum knee joint angle during ground contact, and minimum horizontal pelvis velocity) explained 39% of LEc variability. In addition, four variables (minimum horizontal pelvis velocity, shank touchdown angle, duty factor, and trunk forward lean) combined to explain 31% of the variability in performance (SB time).ConclusionsThis study provides novel and robust evidence that technique explains a substantial proportion of the variance in RE and performance. We recommend that runners and coaches are attentive to specific aspects of stride parameters and lower limb angles in part to optimize pelvis movement, and ultimately enhance performance.

The influence of muscle morphology and strength characteristics on sprint running performance, especially at elite level, is unclear. Purpose: This study aimed to investigate the differences in muscle volumes and strength between male elite sprinters, sub-elite sprinters, and untrained controls; and assess the relationships of muscle volumes and strength with sprint performance. Methods: Five elite sprinters (100 m seasons best [SBE100]: 10.10 ± 0.07 s), 26 sub-elite sprinters (SBE100: 10.80 ± 0.30s) and 11 untrained control participants underwent: 3T magnetic resonance imaging scans to determine the volume of 23 individual lower limb muscles/compartments and 5 functional muscle groups; and isometric strength assessment of lower body muscle groups. Results: Total lower body muscularity was distinct between the groups (controls < sub-elite +20% < elite +48%). The hip extensors exhibited the largest muscle group differences/relationships (elite, +32% absolute and +15% relative [per kg] volume vs sub-elite; explaining 31-48% of the variability in SBE100), whereas the plantarflexors showed no differences between sprint groups. Individual muscle differences showed pronounced anatomical specificity (elite vs sub-elite, absolute volume range +57% to -9%). Three hip muscles were consistently larger in elite vs. sub-elite (TFL, sartorius, gluteus maximus; absolute +45-57% and relative volume +25-37%), and gluteus maximus volume alone explained 34-44% of the variance in SBE100. Isometric strength of several muscle groups was greater in both sprint groups than controls, but similar for the sprint groups and not related to SBE100. Conclusions: These findings demonstrate the pronounced inhomogeneity and anatomically specific muscularity required for fast sprinting, and provides novel, robust evidence that greater hip extensor and gluteus maximus volumes discriminate between elite and sub-elite sprinters and are strongly associated with sprinting performance.

The influence of running speed and sex on running economy is unclear and may have been confounded by measurements of oxygen cost that do not account for known differences in substrate metabolism, across a limited range of speeds, and differences in performance standard. Therefore, this study assessed the energy cost of running over a wide range of speeds in high-level and recreational runners to investigate the effect of speed (in absolute and relative terms) and sex (men vs women of equivalent performance standard) on running economy. To determine the energy cost (kcal · kg−1 · km−1) of submaximal running, speed at lactate turn point (sLTP), and maximal rate of oxygen uptake, 92 healthy runners (high-level men, n = 14; high-level women, n = 10; recreational men, n = 35; recreational women, n = 33) completed a discontinuous incremental treadmill test. There were no sex-specific differences in the energy cost of running for the recreational or high-level runners when compared at absolute or relative running speeds (P > .05). The absolute and relative speed–energy cost relationships for the high-level runners demonstrated a curvilinear U shape with a nadir reflecting the most economical speed at 13 km/h or 70% sLTP. The high-level runners were more economical than the recreational runners at all absolute and relative running speeds (P < .05). These findings demonstrate that there is an optimal speed for economical running, there is no sex-specific difference, and high-level endurance runners exhibit better running economy than recreational endurance runners.

Raw output from deterministic numerical weather prediction models is typically subject to systematic biases. Although ensemble forecasts provide invaluable information regarding the uncertainty in a prediction, they themselves often misrepresent the weather that occurs. Given their widespread use, the need for high‐quality wind‐speed forecasts is well‐documented. Several statistical approaches have therefore been proposed to recalibrate ensembles of wind‐speed forecasts, including a heteroscedastic truncated regression approach. An extension to this method that utilises the prevailing atmospheric flow is implemented here in a quasigeostrophic simulation study and on Global Ensemble Forecasting System (GEFS) reforecast data, in the hope of alleviating errors owing to changes in the synoptic‐scale atmospheric state. When the wind speed depends strongly on the underlying weather regime, the resulting forecasts have the potential to provide substantial improvements in skill relative to conventional post‐processing techniques. This is particularly pertinent at longer lead times, where there is more improvement to be gained over current methods, and in weather regimes associated with wind speeds that differ greatly from climatology. In order to realise this potential, an accurate prediction of the future atmospheric regime is required.

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The transition towards decarbonized power systems requires accounting for the impacts of the climate variability and climate change on renewable energy sources. With the growing share of wind and solar power in the European power system and their strong weather dependence, balancing the energy demand and supply becomes a great challenge. We characterize energy compound events, defined as periods of simultaneous low renewable production of wind and solar power, and high electricity demand. Using a logistic regression approach, we examine the influence of meteorological and atmospheric drivers on energy compound events. Moreover, we assess the spatial coherence of energy compound events that pose a major challenge within an interconnected power grid, as they can affect multiple countries simultaneously. On average, European countries are exposed to winter energy compound events more than twice per year. The combination of extremely low temperatures and low wind speeds is associated with a higher probability of occurrence of energy compound events. Furthermore, we show that blocked weather regimes have a major influence on energy compound events. In particular, Greenland and European blocking lead to widespread energy compound events that affect multiple countries at the same time. Our results highlight the relevance of weather regimes resulting in synchronous spatial energy compound events, which might pose a greater risk within a potential fully interconnected European grid.energy compound events, extremes, renewable energies, weather regimes | INTRODUCTIONClimate change mitigation strategies are driving a global transition towards low carbon energy sources, for which the role of renewable energy is essential (Cronin et al., 2018). To meet the Paris Agreement targets, Europe is undergoing a rapid clean energy transition with an increasing growth in renewable capacity in recent years (IRENA, 2018). Transitioning to a low-carbon energy system will require the impacts of climate variability and

&lt;p&gt;A number of realizations of one or more numerical weather prediction (NWP) models, initialised at a variety of initial conditions, compose an ensemble forecast. These forecasts exhibit systematic errors and biases that can be corrected by statistical post-processing. Post-processing yields calibrated forecasts by analysing the statistical relationship between historical forecasts and their corresponding observations. This article aims to extend post processing methodology to incorporate atmospheric circulation. The circulation, or flow, is largely responsible for the weather that we experience and it is hypothesized here that relationships between the NWP model and the atmosphere depend upon the prevailing flow. Numerous studies have focussed on the tendency of this flow to reduce to a set of recognisable arrangements, known as regimes, which recur and persist at fixed geographical locations. This dynamical phenomenon allows the circulation to be categorized into a small number of regime states. In a highly idealized model of the atmosphere, the Lorenz &amp;#8216;96 system, ensemble forecasts are subjected to well-known post-processing techniques conditional on the system's underlying regime. Two different variables, one of the state variables and one related to the energy of the system, are forecasted and considerable improvements in forecast skill upon standard post-processing are seen when the distribution of the predictand varies depending on the regime. Advantages of this approach and its inherent challenges are discussed, along with potential extensions for operational forecasters.&lt;/p&gt;

A number of realizations of one or more numerical weather prediction (NWP) models, initialised at a variety of initial conditions, compose an ensemble forecast. These forecasts exhibit systematic errors and biases that can be corrected by statistical post‐processing. Post‐processing yields calibrated forecasts by analysing the statistical relationship between historical forecasts and their corresponding observations. This article aims to extend post‐processing methodology to incorporate atmospheric circulation. The circulation, or flow, is largely responsible for the weather that we experience and it is hypothesized here that relationships between the NWP model and the atmosphere depend upon the prevailing flow. Numerous studies have focussed on the tendency of this flow to reduce to a set of recognisable arrangements, known as regimes, which recur and persist at fixed geographical locations. This dynamical phenomenon allows the circulation to be categorized into a small number of regime states. In a highly idealized model of the atmosphere, the Lorenz ‘96 system, ensemble forecasts are subjected to well‐known post‐processing techniques conditional on the system's underlying regime. Two different variables, one of the state variables and one related to the energy of the system, are forecasted and considerable improvements in forecast skill upon standard post‐processing are seen when the distribution of the predictand varies depending on the regime. Advantages of this approach and its inherent challenges are discussed, along with potential extensions for operational forecasters.