[1] Statistics of microstructure patches in a sheared, strongly stratified metalimnion of Lake Banyoles (Catalonia, Spain), which occupied ∼40% of the total lake depth of 12 m, are analyzed. Light winds (<3 m s −1 ) dominated the periods of observation in late June and early July of 2009. The patch sizes h p and the corresponding patch Thorpe scales L Tp were identified using profiling measurements of temperature microstructure and small-scale shear. The distribution of h p was found to be lognormal with mean and median values of 0.69 m and 0.50 m respectively. The distribution of L Tp within the patches was also fitted to a lognormal model and the mean and median values found to be close to 0.1 m. The probability distribution of the ratio L Tp /h p was approximated by the Weibull probability model with a shape parameter c w ≈ 2, and also by beta probability distribution. For h p > 0.25 m, the ratio L Tp /h p depends on the patch Richardson and mixing Reynolds numbers following the parameterization of Lozovatsky and Fernando (2002). Analysis of the dynamics of mixing reveals that averaged vertical diffusivities ranged between ∼1 × 10 −4 m 2 s −1 and ∼5 × 10 −5 m 2 s −1, depending on the phase of the internal waves. Episodic wind gusts (wind speed above 6 m s −1 ) transfer ∼1.6% of the wind energy to the metalimnion and ∼0.7% to the hypolimnion, generating large microstructure patches with h p of several meters.Citation: Planella Morato, J., E. Roget, and I. Lozovatsky (2011), Statistics of microstructure patchiness in a stratified lake,
Prophylactic antithrombotic therapy was according the reference protocol in 67% of cases. In older patients, with greater risk of thromboembolic disease, the adequacy is worse.
Numerous studies have explored whether and how the spread of the coronavirus disease 2019 (COVID-19) responds to environmental conditions without reaching unique or consistent answers. Sociodemographic factors such as variable population density or mobility as well as the lack of effective epidemiological monitoring difficult establishing robust correlations. Here we carry out a regional cross-correlation study between nine atmospheric variables and an infection index (Ic) estimated from standardized positive polymerase chain reaction (PCR) test cases. The correlations and associated time-lags are used to build a linear multiple-regression model between weather conditions and the Ic index. Our results show that surface pressure and relative humidity can predict COVID-19 outbreaks during periods of relatively minor mobility and meeting restrictions. The occurrence of low-pressure systems, associated with the autumn onset, leads to weather and behavioral changes that intensify the virus transmission. These findings suggest that surface pressure and relative humidity are key environmental factors in the seasonal dynamics of the COVID-19 spread, which may be used to improve COVID-19 forecast models.
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