Question

Cloud Cover in San Francisco-Online The plots in Exercise 2.258 on cloud cover in San Francisco can be found online at weatherlines. zanarmstrong.com if you prefer Figure \(2.99(\mathrm{a})\) or weather.zanarmstrong.com if you prefer Figure \(2.99(\mathrm{~b}) .\) In the interactive display you can hover over points to get more information. You can also click on the map to change the city or the drop down menu to change the weather statistic that is plotted. Use the interactive plots at this website to answer the questions below. (a) In San Francisco, approximately what time of day has the highest percent cloud cover in August? (b) Which season tends to be the least windy for Chicago (the "Windy City")?

Short answer

Since this exercise requires interaction with a live web-based data visualization tool, the specific answers to part (a) and (b) cannot be provided in a JSON file, without directly interacting with the website. Therefore, the short answer to this question can be obtained only by following the step by step solution to interact with the data visualization tool and derive the required conclusion.

Step-by-step solution

Open the Website and Select San Francisco

Navigate to either of the provided websites, weatherlines.zanarmstrong.com or weather.zanarmstrong.com depending on the desired figure. After successfully loading the site, navigate to the city selection area and choose San Francisco. The weather data for San Francisco will now be displayed.

Analyze the Cloud Cover for August

Find the option which refers to 'time of day' and 'cloud cover'. Afterwards, locate the area that corresponds to the month of August. Observe the graph to identify the time of day during which there is the highest percent cloud cover in August. This percentage and the corresponding time will be the answer to part (a) of the question.

Change the City to Chicago and Analyze the Windy Season

Now repeat the process for the second part of the question but this time, select the 'Chicago' from the city selector. Next, find and click on the option which leads to 'wind speed' or something similar that refers to windiness. Observe the graph as you did previously, but now look for the season that displays the least windiness. This season will be the answer to the second part of the question.

Key concepts

Weather Data Analysis

Understanding weather patterns is crucial for a myriad of applications ranging from agriculture to urban planning. Weather data analysis involves examining the vast amounts of data collected by weather stations and satellites to discern trends and make predictions.

Interactive data visualization tools like the ones mentioned in the exercise make this analysis more accessible by allowing users to explore weather statistics through clickable charts and graphs. With these tools, a user can hover over data points to receive instant information or select specific parameters to generate custom visualizations. This direct interaction not only aids in learning but also makes complex data comprehensible.

• Interactive charts can showcase temperature changes throughout the day or year.
• Users might compare precipitation levels across different regions.
• Graphs could be used to visualize wind speed trends or extreme weather events over time.

By making data manipulation straightforward, students and analysts alike can discover patterns that inform decision-making, ranging from what to wear on a given day to creating long-term climate models.

Cloud Cover Patterns

Cloud cover is an essential aspect of meteorology as it influences local climate conditions, including temperature and precipitation. Cloud cover patterns exhibit considerable variation and can be tracked through both direct observation and remote-sensing technologies.

Through interactive data visualizations, these patterns can be mapped over time to show the frequency and extent of cloud cover. For instance, coastal cities like San Francisco often experience foggy mornings due to temperature variations between land and ocean, which can be beautifully illustrated in a time-lapse visualization of cloud cover.

Key Points about Cloud Cover Patterns:

• They can indicate incoming weather systems.
• Different cloud types have different implications for weather; for example, cumulonimbus clouds often precede storms.
• Understanding patterns aids in predicting weather conditions for aviation, agriculture, and outdoor events.

The exercise provided involves analyzing such patterns to determine when cloud cover is densest, which can be relevant for industries reliant on natural light, such as solar energy production.

Seasonal Wind Patterns

Wind is a dynamic element of our planet's climate system and is affected by factors such as the Earth's rotation, topography, and temperature gradients. Seasonal wind patterns change due to the tilt of the Earth's axis and its orbit around the sun, leading to variations in temperature and pressure that influence wind behavior.

For instance, cities like Chicago experience distinct wind patterns that can be attributed to the inland location and the impact of the Great Lakes. Students can employ interactive data visualization tools to observe these seasonal shifts. Examining wind speed data from various seasons can yield insights into the best times of year for activities like sailing or flying kites.

Factors Affecting Wind Patterns:

• Geographical location and surface features dictate the prevailing winds.
• Seasonal changes can alter wind patterns due to temperature differences between land and water masses.
• Local wind patterns might influence urban planning, for example, in terms of building orientations or placing wind turbines.

The interactive exercise demonstrates how to leverage such data visualizations to determine when Chicago, often nicknamed the 'Windy City', experiences lower wind speeds, thereby dispelling or confirming popular notions about its breezy reputation.