... | @@ -21,27 +21,6 @@ graph LR |
... | @@ -21,27 +21,6 @@ graph LR |
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## [R2_em.r&R2_winds.r](R2_em.r&R2_winds.r)
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## [R2_em.r&R2_winds.r](R2_em.r&R2_winds.r)
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(5) Regression coefficients for increasing thresholds [R2_em.r][R2_winds.r]
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The following scores for the regression DOD.vs.emissions are computed for increasing thresholds of DOD and emissions (R2_em.r):
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* Regression coefficient (R²)
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* Slope
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* Intercept
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* Standard deviation of the slope
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* Standard deviation of the intercept
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* RMSE
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The coefficients are determined by seasons and by month individually. And the vectors of increasing thresholds for emissions and DOD are:
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```R
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od_steps <- c(0, 1:9 %o% 10^(-(4:0)))
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od_steps <- c(od_steps,c(1:9 %o% 10^((1)))) #from 10^-4 to 90 in a logarithmic way (0 included)
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em_steps <- c(0, 1:9 %o% 10^(-(11:-2))) #from 10^-11 to 9*10⁻2 in a logarithmic way (0 included)
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```
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The final output are then matrixes of
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The exact same procedure is done filttering by winds (u*) instead of emissions:
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```R
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wind_steps <- seq(from = 0, to = 1.5, by = 0.025) #from 0 to 1.5 m/s by steps of 0.025 m/s
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```
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## Data analysis of other regions
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## Data analysis of other regions
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The data analysis done in this work has only considered data referring North Africa.
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The data analysis done in this work has only considered data referring North Africa.
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