Cloud Seeding

Author

K M

Project 3

reading in csv file

clouds = read.csv("~/Desktop/GEOG 5680/Data/clouds.csv")

1.

seeding = subset(clouds, seeding == "yes")
no_seeding = subset(clouds, seeding == "no")

summary(seeding$rainfall)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.090   2.683   4.530   4.634   5.468  11.860

sd(seeding$rainfall)

[1] 2.776841

var(seeding$rainfall)

[1] 7.710845

summary(no_seeding$rainfall)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  0.280   1.075   4.055   4.172   5.832  12.850

sd(no_seeding$rainfall)

[1] 3.519196

var(no_seeding$rainfall)

[1] 12.38474

boxplot(rainfall~seeding, clouds)

t.test(clouds$rainfall~clouds$seeding)


    Welch Two Sample t-test

data:  clouds$rainfall by clouds$seeding
t = -0.3574, df = 20.871, p-value = 0.7244
alternative hypothesis: true difference in means between group no and group yes is not equal to 0
95 percent confidence interval:
 -3.154691  2.229691
sample estimates:
 mean in group no mean in group yes 
         4.171667          4.634167

For the t-test, there is no significant difference, meaning that the difference between the average amount of rainfall for both the seeded and non-seeded clouds is not meaningful.

2.

lm(rainfall~ seeding, data = clouds)


Call:
lm(formula = rainfall ~ seeding, data = clouds)

Coefficients:
(Intercept)   seedingyes  
     4.1717       0.4625

clouds.lm = lm(rainfall ~ seeding + cloudcover + prewetness + echomotion + sne,
   data = clouds)
summary(clouds.lm)


Call:
lm(formula = rainfall ~ seeding + cloudcover + prewetness + echomotion + 
    sne, data = clouds)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.1158 -1.7078 -0.2422  1.3368  6.4827 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)           6.37680    2.43432   2.620   0.0174 *
seedingyes            1.12011    1.20725   0.928   0.3658  
cloudcover            0.01821    0.11508   0.158   0.8761  
prewetness            2.55109    2.70090   0.945   0.3574  
echomotionstationary  2.59855    1.54090   1.686   0.1090  
sne                  -1.27530    0.68015  -1.875   0.0771 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 2.855 on 18 degrees of freedom
Multiple R-squared:  0.3403,    Adjusted R-squared:  0.157 
F-statistic: 1.857 on 5 and 18 DF,  p-value: 0.1524

anova(clouds.lm)

Analysis of Variance Table

Response: rainfall
           Df  Sum Sq Mean Sq F value  Pr(>F)  
seeding     1   1.283  1.2834  0.1575 0.69613  
cloudcover  1  15.738 15.7377  1.9313 0.18157  
prewetness  1   0.003  0.0027  0.0003 0.98557  
echomotion  1  29.985 29.9853  3.6798 0.07108 .
sne         1  28.649 28.6491  3.5158 0.07711 .
Residuals  18 146.677  8.1487                  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Although there are no significant p-values, echomotion and SNE appear to influence rainfall the most.

3.

seeding.lm = lm(rainfall~sne, data = seeding)
summary(seeding.lm)


Call:
lm(formula = rainfall ~ sne, data = seeding)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0134 -1.3297 -0.3276  0.6171  4.3867 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)   
(Intercept)  12.0202     2.9774   4.037  0.00237 **
sne          -2.2180     0.8722  -2.543  0.02921 * 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 2.27 on 10 degrees of freedom
Multiple R-squared:  0.3927,    Adjusted R-squared:  0.332 
F-statistic: 6.467 on 1 and 10 DF,  p-value: 0.02921

There is a significant difference (p = 0.0292), meaning that SNE affects rainfall if seeding occurred. The slope tells us that for every 1 unit increase in SNE, we can expect rain to decrease by 2.22 if seeding occurred.

no_seeding.lm = lm(rainfall~sne, data = no_seeding)
summary(no_seeding.lm)


Call:
lm(formula = rainfall ~ sne, data = no_seeding)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.4892 -2.1762  0.2958  1.4902  7.3616 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)  
(Intercept)    7.319      3.160   2.317    0.043 *
sne           -1.046      0.995  -1.052    0.318  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 3.502 on 10 degrees of freedom
Multiple R-squared:  0.09957,   Adjusted R-squared:  0.009528 
F-statistic: 1.106 on 1 and 10 DF,  p-value: 0.3177

There is no significant difference (p = 0.318), meaning that SNE doesn’t affect rainfall if seeding didn’t occur. The slope tells use that for every 1 unit increase in SNE, we can expect rain to decrease by 1.05 if no seeding occurred.

clouds$colors = clouds$seeding
clouds$colors[clouds$seeding == "yes"] = "darkgreen"
clouds$colors[clouds$seeding == "no"] = "red"

plot(clouds$sne, clouds$rainfall,
     col = clouds$colors, pch = 20,
     xlab = "Suitability Criterion (SNE)", ylab = "Rainfall ((m^3)*1e+8)",
     main = "Scatterplot of SNE and Rainfall by Seeding")

legend("topright",
       legend = c("Seeding", "No Seeding"),
       pch = 20,
       col = c("darkgreen", "red"))

abline(seeding.lm[1], seeding.lm[2], col = "darkgreen")
abline(no_seeding.lm[1], no_seeding.lm[2], col = "red")

The difference in slopes suggests that rainfall is greater in places with a lower SNE when clouds are seeded compared to when they are not seeded. However, as SNE increases (specifically SNE > 4), seeded clouds will produce less rain than non-seeded clouds.