You Will Create a Data Chart That Looks Like This

Estimating Cloud Cover(Lab #24)

Name______Date______

Purpose- The purpose of this lab is to better understand percentage of cloud cover and to take more accurate cloud cover observations.

Variables-

Data and Observations You will create a data chart that looks like this Per 3A

Name of Estimated Classification Right Wrong Classmate Percent Bobby Laura Jackie Nicole Bryce Kaylee Jozie Jay Kaila Sergio Taylor Stephanie Jacob

Percentage If Less Than If greater or equal to 10% Clear Isolated 25% Isolated Scattered 50% Scattered Broken 90% Broken Overcast Name of Actual Underestimates Correct Overestimates Classmate % Estimates Bobby 10 0 11 0 Laura 50 6 2 3 Jackie 20 0 9 2 Nicole 20 0 3 8 Bryce 30 1 3 7 Kaylee 30 0 6 5 Josie 30 1 6 4 Jay 20 0 6 5 Kaila 50 0 6 5 Sergio 20 4 5 2 Taylor 50 4 4 3 Stephanie 30 3 5 3 Jacob 60 6 3 2

Name of Correct Class too low Class Class Classmate Class Correct too High Bobby Isolated 0 11 0 Laura Broken Jackie Isolated Nicole Isolated Bryce Scattered Kaylee Scattered Jozie Isolated Jay Isolated Kaila Broken Sergio Isolated Taylor Broken Stephanie Scattered Jacob Broken You will create a data chart that looks like this Per 3B

Name of Estimated Classification Right Wrong Classmate Percent Alexis Melissa Lindsay Chris Ayana Carl Harold Jorge Brandon Amir Holly

Percentage If Less Than If greater or equal to 10% Clear Isolated 25% Isolated Scattered 50% Scattered Broken 90% Broken Overcast Name of Actual Underestimates Correct Overestimates Classmate % Estimates Alexis 20 2 1 5 Melissa 40 4 1 3 Lindsay Chris 20 5 3 0 Ayana 40 2 4 2 Carl 40 3 2 2 Harold 30 4 2 2 Jorge 10 0 8 0 Brandon 20 0 8 0 Amir 50 3 5 0 Holly 30 7 1 0

Name of Correct Class too low Class Class Classmate Class correct too low Alexis Melissa Lindsay Chris Ayana Carl Harold Jorge Brandon Amir Holly You will create a data chart that looks like this Per 8/9A

Name of Estimated Classification Right Wrong Classmate Percent Joe Evan Kristen Alexis Raina J.P Emily Grant Chelsea Elmanda Dan Lindsay Mike

Percentage If Less Than If greater or equal to 10% Clear Isolated 25% Isolated Scattered 50% Scattered Broken 90% Broken Overcast Name of Actual Underestimates Correct Overestimates Classmate % Estimates Joe 40 3 3 6 Evan 20 6 5 1 Kristen 50 8 2 2 Alexis 50 4 3 5 Raina 10 0 1 11 J.P 20 4 5 3 Emily 40 6 3 3 Grant 20 3 7 2 Chelsea 40 7 2 3 Elmanda 20 1 8 3 Dan 40 7 2 3 Lindsay 20 5 6 1 Mike 10 0 12 0

Name of Correct Class too low Class Class to Classmate Class Correct low Joe Evan Kristen Alexis Raina J.P Emily Grant Chelsea Elmanda Dan Lindsay Mike You will create a data chart that looks like this Per 8/9B

Name of Estimated Classification Right Wrong Classmate Percent Matt B Kristen Taylor Kim Marc Jake Matt P Jessica Nicole Ron Steve Ethan Sierra

Percentage If Less Than If greater or equal to 10% Clear Isolated 25% Isolated Scattered 50% Scattered Broken 90% Broken Overcast Name of Actual Underestimates Correct Overestimates Classmate % Estimates Matt B Kristen Taylor Kim Marc Jake Matt P Jessica Nicole Ron Steve Ethan Sierra

Name of Correct Class too low Class Class Classmate Class correct too high Matt B Kristen Taylor Kim Marc Jake Matt P Jessica Nicole Ron Steve Ethan Sierra You will create a data chart that looks like this Per 12A

Name of Estimated Classification Right Wrong Classmate Percent Brandi Danielle Kyle Ambrie Jenn Lloyd Julie Ashley Mary Wes Rashaad Roxayne

Percentage If Less Than If greater or equal to 10% Clear Isolated 25% Isolated Scattered 50% Scattered Broken 90% Broken Overcast Name of Actual Underestimates Correct Overestimates Classmate % Estimates Brandi 60 1 2 6 Danielle 50 3 2 4 Kyle 40 3 1 5 Ambrie 30 1 5 3 Jenn 60 4 4 1 Lloyd 50 8 1 0 Julie 60 6 1 2 Ashley 20 6 3 0 Mary 50 6 2 1 Wes 40 4 5 0 Rashaad 20 4 5 0 Roxayne 60 4 2 3

Name of Correct Class too low Class Class to Classmate Class correct high Brandi Danielle Kyle Ambrie Jenn Lloyd Julie Ashley Mary Wes Rashaad Roxayne You will create a data chart that looks like this Per 12B

Name of Estimated Classification Right Wrong Classmate Percent Autumn Alex Daniella Ben Diego Sabrina Tyler Tuesday Victor Megan Aimee Emily Cody

Percentage If Less Than If greater or equal to 10% Clear Isolated 25% Isolated Scattered 50% Scattered Broken 90% Broken Overcast Name of Actual Underestimates Correct Overestimates Classmate % Estimates Autumn 30 2 7 3 Alex 30 7 3 2 Daniella 40 2 2 8 Ben 30 8 3 1 Diego 60 5 3 4 Sabrina 40 0 0 12 Tyler 30 3 2 7 Tuesday 60 10 0 2 Victor 50 7 4 1 Megan 10 0 12 0 Aimee 50 7 2 3 Emily 50 7 4 1 Cody 40 9 3 0

Name of Correct Class too Low Class Class Classmate Class correct too high Autumn Alex Daniella Ben Diego Sabrina Tyler Tuesday Victor Megan Aimee Emily Cody Conclusion Questions

1. Where did the greatest errors occur?

2. Does the class have a tendency to overestimate or underestimate cloud cover?

3. What factors influenced the accuracy of the estimates?

4. Do you feel you have a talent for this or is something you have to learn? Explain…..

5. Where else might such spatial estimation skills be valuable?

6. Which cloud classifications were the easiest and most difficult to identify?

7. What strategies enable you to correctly estimate cloud cover?

8. What strategies might produce more accurate classifications?