Storyboard

The house will be exposed to various environmental factors, and therefore, its design must take into account the different aspects that will affect it in order to find the best strategies to mitigate their effects.

• Particular attention should be given to the effects of wind and rain, with a focus on minimizing water entry through doors, windows, and potential leaks.

• Furthermore, the exposure to temperature and wind should be considered as they impact the thermal resistance of the house, consequently affecting heating costs.

Below, we present how to obtain historical weather information from the openweathermap.org website and an initial exploration of the data for this project. The same process can be carried out for any other location for which the corresponding data is acquired.

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To access the application, you have two options:

1. Using the images on the right side of the main page:

• Click on the Spyrit logo in the upper-left corner to access the main page.
• Once on the main page, you can browse through the images located on the right side.
• Use the "<" (left) and ">" (right) arrows to navigate between the images.
• When you find the desired image, click on it, and the application will load.

2. Using the navigation menu on the left side:

• Go to the "Knowledge" section in the navigation menu on the left side.
• Then, select "Palos Verdes" from the dropdown menu.
• Next, choose the "Clima" option within the Palos Verdes section.
• Select "Caracterización" in the submenu.
• Finally, choose "Datos climáticos clave".
• By following these steps, the application will load.

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To be able to access the downloaded data from openweathermap.org (see related presentation), you need to first select the file. To do this, click on the "Load" button and locate the CSV file:

After confirming, the data will be loaded and available for further exploration.

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Once the data is selected for visualization, it is presented in both graphical form, indicating the average value, the average value plus and minus the standard deviation, and a table with the numerical values:

The data is structured as follows:

• Annual averages across all months.

• Monthly averages across all years.

The graphical representation allows us to visualize possible trends of more systematic changes, such as climate change or environmental variations. The monthly analysis provides insights into how the parameter changes throughout the year.

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Once the data is selected for visualization, it is presented both graphically, displaying the mean value, the mean value plus the standard deviation at the top, and the mean value minus the standard deviation at the bottom. It is also presented in a table with the numerical values:

The data is structured as follows:

• Yearly averages across all months.
• Monthly averages across all years.

The equation for calculating the mean is:

$\bar{x}=\displaystyle\frac{1}{N}\displaystyle\sum_{i=1}^N x_i$

Where $x_i$ represents the $i$-th value and $N$ is the number of values.

Additionally, the standard deviation is calculated to provide an understanding of the variability present in the mean value, that is, how much it can vary. The standard deviation $\sigma$ is calculated using the following equation:

$\sigma^2=\displaystyle\frac{1}{N}\displaystyle\sum_{i=1}^N x_i^2 - \bar{x}^2$

In the case of a Gaussian distribution of the data, it holds true that 34.1% of the data falls between the values $\bar{x}-\sigma$ and the mean value $\bar{x}$, and likewise, 34.1% falls between the mean value $\bar{x}$ and $\bar{x}+\sigma$. This means that in this case, 68.2% of the data lies within the range of $\bar{x}-\sigma$ to $\bar{x}+\sigma$:

It is important to note that:

The distribution is not necessarily Gaussian.

If the distribution is not Gaussian, the range $[\bar{x}-\sigma,\bar{x}+\sigma]$ may contain values that do not make sense. For example, there may be negative values or values greater than 100% when the range should be within $[0,100]$.

Therefore, the standard deviation should be interpreted as a measure of the dispersion of the values, that is, how likely they are to be found around the mean value.

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The annual average 11.3°C displays a relatively stable value without showing any anomalous trends:

Similarly, the standard deviation remains constant, indicating a consistent level of fluctuation without any notable variation.

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The monthly average clearly shows the profile of a location in the southern hemisphere, with a peak in summer around 14.3°C in February and a minimum around 8.7°C in July:

However, it is important to note that the standard deviation is not negligible, which means that an individual July month could have a significantly lower average temperature (e.g., 7°C) or a higher average temperature (e.g., 10°C). This indicates that there can be significant variations in temperatures within the same month.

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The average wind speed has remained around 5.9 m/s over the years, without showing any anomalous trends:

Similarly, the standard deviation remains constant, indicating no tendency to vary in fluctuations.

The value can be considered high and is directly influenced by the location being situated on the coastline.

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In the monthly average table, it can be observed that wind speed shows a slight decrease in autumn and an increase in winter:

However, this variation is relatively small compared to the standard deviation.

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The hourly rainfall remains around 0.96 mm/h over the years, without showing any anomalous trends:

Similarly, the standard deviation remains constant, indicating no tendency to vary in the fluctuations.

In this case, the lower curve reaches negative values, which does not make sense. This reflects that the distribution does not follow a Gaussian shape, and therefore, the standard deviation does not necessarily represent the width of a bell curve, as discussed earlier.

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In the monthly average table, it can be observed that the amount of rainfall per hour experiences a slight increase in winter:

However, this variation is relatively small compared to the standard deviation.

In this case, the lower curve reaches negative values, which does not make sense. This reflects that the distribution does not follow a Gaussian shape, and therefore, the standard deviation does not necessarily represent the width of a bell curve, as discussed earlier.

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The fraction of time that it rains has remained around 24.8% over the years, without showing any anomalous trends:

Similarly, the standard deviation remains constant, indicating no tendency to vary in the fluctuations.

In this case, the lower curve reaches negative values, which does not make sense. This reflects that the distribution does not follow a Gaussian shape, and therefore, the standard deviation does not necessarily represent the width of a bell curve, as discussed earlier.

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In the monthly average table, it can be observed that the fraction of time it rains is lower in summer, around 10%, and increases in winter, reaching approximately 44%:

The variation between summer and winter is of the same order as the standard deviation, indicating a significant change that goes beyond the range of normal fluctuations.

In this case, the lower curve reaches negative values, which does not make sense. This reflects that the distribution does not follow a Gaussian shape, and therefore, the standard deviation does not necessarily represent the width of a bell curve, as discussed earlier.

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The cloud cover has remained around 51.9% of the time over the years, without showing any anomalous trends:

Similarly, the standard deviation remains constant, indicating no tendency to vary in the fluctuations.

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In the monthly average table, it can be observed that the cloud cover is lower in summer, around 33%, and higher in winter, approximately 67.7%:

However, this variation is relatively small compared to the standard deviation.

In this case, the lower curve reaches negative values, which does not make sense. This reflects that the distribution does not follow a Gaussian shape, and therefore, the standard deviation does not necessarily represent the width of a bell curve, as discussed earlier.

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The average humidity has remained around 83.9% of the time over the years, without showing any anomalous trends:

Similarly, the standard deviation remains constant, indicating no tendency to vary in the fluctuations.

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In the monthly average table, it can be observed that the average humidity only fluctuates minimally around the annual average of 83.9%:

This is likely due to the proximity to the ocean, which, combined with a relatively stable temperature, maintains a consistently high humidity throughout the year.

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In the case of the annual average atmospheric pressure, a slight upward trend (from 1017 to 1018 bar) can be recognized, although it is much smaller than the standard deviation, so it may not be necessarily significant:

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In the monthly average table, it can be observed that the atmospheric pressure is lower in summer and tends to increase until spring, after which it decreases again:

The increase between summer and spring is of the same order as the standard deviation of summer, indicating a significant effect.

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