There is more water in the air, or there is more capacity for more water, when the air is warmer. I often hear people talk about how it was a very humid day, when in reality the relative humidity was actually lower than normal or lower than the previous day. The key word used here is “relative”. Relative Humidity (RH) is a measurement of the amount of moisture in the air and is expressed as a percentage. If the RH is 100%, the air is saturated and condensation occurs taking the form of fog or dew. While our bodies can “feel” more moisture in the air, the reality is that an RH of 100% feels much different at 40 degrees compared to 90 degrees.
So, what does the word “relative” mean?
Relative to what?
It is relative to the temperature. The higher the air temperature, the more moisture the air can hold. A more specific (not relative) measurement of the amount of moisture in the air is the Dewpoint Temperature (Td). Td is defined as the temperature at which dew forms (condensation occurs) if the air temperature is cooled (keeping pressure constant).
While this all may seem like semantics, or not very important for our everyday use of weather, the amount of moisture in the air can have an enormous impact on processes in our world.
For example, the amount of moisture in the air has a direct impact on the growth or lack of growth in plants. So, when predicting the growth or creating a predictive model about plant harvest, taking into account the amount of moisture in the air on a daily, weekly, monthly, or seasonal period can be significantly important to those calculations. Rather than using an average RH for a given day, since that average does not accurately reflect the “average” amount of moisture in the air. Td is much more appropriate for determining the amount of moisture in the air and averaging the Td will be more representative of the amount of moisture over the time period in question.
Data analytics (typically from “big data” sets), pulling information from historical data, oftentimes with the goal of creating a predictive model for future use, is a relatively new field in such industries such as retail sales, advertising, and agriculture. Weather is often an important component in predictive models. Understanding the weather variables, knowing their strengths and limitations when using them, is critical to getting the appropriate answer to a problem. WDT not only understands the weather variables, but can help you solve your weather data needs whatever the weather-related analytic.