5, 10, 20, and 30 days before the date when the DMS image was
five, 10, 20, and 30 days before the date when the DMS image was taken, Antibacterial Compound Library MedChemExpress considering the accumulative effects of those explanatory variables. After exploring all achievable many linear regression models, we discovered that dynamicthermodynamic variables integrated by 10 days showed the highest correlation coefficient. Consequently, these explanatory variables have been utilised to reconstruct the linear regression models using the forward and backward stepwise regression strategy. The coefficients of all normalized explanatory variables for all models are illustrated in Table 9. There have been 11 thermodynamic-dynamic variables, including one particular thermodynamic variable (temperature), six dynamic variables (velocity of wind and ice motion), and four Fadrozole Technical Information kinetic moments brought on by ice motion.Table 9. Chosen variables and coefficients in 14 stepwise linear regressions.Year 2012 2013 2014 2015 2016 2017 2018 Strategy Forward Backward Forward Backward Forward Backward Forward Backward Forward Backward Forward Backward Forward Backward R2 0.26 0.26 0.48 0.48 0.87 0.87 0.34 0.34 0.29 0.34 0.66 0.66 0.30 0.30 Tmp10 / 0.ten U10_10 / / / / V10 _10 / / / / Wind_10 / / 0.35 0.35 1.09 / / / / 4.09 six.77 6.86 1.83 1.72 U_Ice_10 V_Ice_10 Vel_Ice_10 0.16 / 9.51 9.45 Div10 Vor10 Shr10 / / Stc10 / / / / / / Continual 0.41 0.31 0.60 0.-0.39 -0.34 -6.46 -6.1.24 1.-0.38 -0.19 -2.78 -2.15.31 13.34 / / / /-0.10 -0./ / / -0.16 0.15 0.15 0.30 /-0.08 / -0.01 /0.89 0.87 0.14 0.-1.19 -1.four.61 four.64 / / / 0.-0.14 -0.15 -0.55 -0.0.28 0.28 0.57 0.46 / / / /-5.60 -5./ /-0.97 / -0.53 -0./ -0.-12.98 -11.1.19 1.19 0.29 /-2.08 -1.0.40 0.40 0.21 0.22 1.50 1.45 0.45 0.-1.35 -1./ / 2.98 3.02 / /-0.33 -0.0.15 / / /-0.79 -4.62 -6.54 -6.57 -1.40 -1.-0.39 -0./ /-1.17 -1.0.34 0.-3.08 -3.11 -1.40 -1.-0.09 // /-2.01 -2./ /-0.19 -0./ /-0.03 /-0.31 -0.The forward and backward stepwise regression models for each year identified unique sets of explanatory variables. Each 2012 models identified ice motion velocity and divergence because the considerable explanatory variables. The 2013 models mostly identified the ice motion velocity and temperature variables. Aside from ice motion velocity and temperature, the 2014 models integrated wind velocity at u-direction, as well as the correlation coefficient was significantly greater than that of other models. The 2015 models emphasized the functions of wind and ice motion velocity. The 2016 forward model identified much more kinetic moments, however the backward model emphasized wind velocity, which represents the doable correlation amongst these variables. Lastly, the 2017 and 2018 models showed significant influence of wind velocity and temperature. Except for that of 2014, all other models had only moderate correlation, and R2 ranged from 0.26 to 0.66. This was due to the fact (1) the sea ice fractions were derived from higher spatial resolution DMS photos, along with the dynamic-thermodynamic variables had a much coarser resolution of 25 km; (two) the atmospheric and oceanic dynamics that contribute to lead formation can happen within a much smaller sized scale (25 km scale), which can not be captured by coarse resolution items; and (3) the uncertainty with the DMS-based lead detection (accuracy of 90 ) may be carried and exaggerated within the information fusion and resampling procedure. Depending on all stepwise regression outcomes, the relative explanatory variable significance could be ranked determined by their frequencies within a total of 14 regression models (Table 9), as summarized in Figure ten. It showed that temperature and ice motion vor.