Precipitation Coverage is a metric commonly encountered in weather data yet its meaning is often misunderstood. In this post I will discuss the differences and similarities between Precipitation and Precipitation Coverage, and how to gain significant additional value from using Precipitation Coverage in your own analysis.
Before discussing Precipitation Coverage, it is important to have a solid understanding of the basic Precipitation metric itself. The Precipitation metric in weather data tells us total amount of any precipitation that reach the ground during the time period. In the case of rain, this is simply the number of millimeters, centimeters, or inches of water that hit the ground at a given point. If you have a straight-sided cup and set it out in during the time period in question, you should be able to measure the depth of the water at the end of the period and get this same Precipitation number.
Precipitation measuring gets a little more complex for snow and ice. Instead of measuring the depth of the snow or ice during the period, the Precipitation metric gives the amount of water that is contained in the snow or ice. If we use our straight-sided cup above to collect snow data, we first need to set it out during the measurement period. Then, after the collection is finished, we need to melt the contents before we take the measurement. Note that this means the Precipitation metric value is typically much lower than the measurement of the snow that fell. The actual difference between these two metrics can vary widely from event to event and place to place. In the US NOAA says that the average multiple is about 13. That is, it takes 13 inches of snow to equal one inch of water.
Precipitation Coverage is a metric that is more misunderstood than basic Precipitation but it actually simpler in some respects. Precipitation Coverage tells us the percentage of time during the reporting window that the precipitation actually occurred. That is, if Precipitation Coverage is 50%, that means half of the time in the period saw precipitation while the other half did not.
This can be a very useful metric for various types of analysis, especially when combined with the actual precipitation amount. For example, consider a one-day reporting window in which the report says two inches of rain fell. If I’m looking at that data alone on a spreadsheet, I can only guess if those two inches fell due to a short-term thunderstorm or if it instead fell as a slow drizzle soaking the entire day. In the former case, some specific activities may have been cancelled or delayed, but most of the day’s events would have been able to proceed as scheduled. In the latter case, the entire day was likely ruined for outdoor activities. People would have tended to stay home whenever possible.
Luckily, Precipitation Coverage allows me to better guess what really happened that day. Consider, the case where Precipitation Coverage is only 10%. That tells us that some sort of cloudburst storm rolled in and downpoured for only a few hours. The rest of the day, while perhaps wet underfoot, was probably not an obstacle for people wanting to get outside, travel, and do activities. On the other hand, it the Precipitation Coverage metric is 90%, that means almost the entire day was rainy. Of course, the rain fell more slowly, and people who ventured outside got “less wet” during any given outdoor period then they would have in a downpour. However, since most people don’t like being in the rain at all, and the entire day would have felt soggy, it is reasonable to assume that most outdoor and travel activities would have seen low attendance numbers.
This is important for many types of analysis. Consider that a cloudburst (higher Precipitation value and lower Precipitation Coverage value) is more likely to cause flash flooding or people to be soaked outdoors unexpectedly without an umbrella. On the other hand, a soaking rain (high precipitation coverage), is likely to lead to the cancellation of entire events, encourage people to stay home, and trigger an increase in activities such as movie watching and online shopping. Considering a nature point of view, the soaking rain is far more valuable to farmers and gardeners.
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While Precipitation is a measure of the amount of liquid that fell from the sky over the entire reporting period, Precipitation Coverage gives us additional insight as to the manner in which that precipitation fell. The higher the Precipitation Coverage metric, the longer the precipitation lasted and the slower it fell. In some applications, knowing a cloudburst from a soaking rain adds significant value to the analysis. While some weather data sources provide only Precipitation data, the Microsoft Excel Weather Add-in provides both Precipitation and Precipitation Coverage data for tens of thousands of locations across decades. This allows the user to have a better understand of a precipitation event and better understand its ramifications on their own data.
Although sometimes impossible due to the data or tools available, you should also consider using hourly data for precipitation analysis as much as possible. More advanced tools such as Visual Crossing Weather not only provide hourly reporting but also can allow reporting and analysis based on custom “business hours” in some versions. After all, there is often little need to consider precipitation the fell in the middle of the night if you are analyzing a business that is only open from 9am-5pm or planning baseball practices that are only possible in the afternoons after school.