Report-back:
Can satellites tell rain from snow?
Can satellites tell rain from snow?
February 2023 - Led by Hailey Bogle and Anne Nolin
Written by Nayoung Hur
Satellite outputs are produced in different ways. We typically see them in the form of imagery (think of your maps or exploring on Google Earth), but the Mountain Rain or Snow scientists are interested in satellite outputs that tell us what is falling from the sky. If you have been an observer with us, you will know that this question is what drives our work.
NASA has a satellite mission called GPM, which stands for the Global Precipitation Measurement. GPM produces maps of rainfall in near-real time across the globe. Scientists are also trying to see if GPM can distinguish between rainfall and snowfall – but they need to have observations in order to verify the satellite measurements. Interestingly, there is no instrument that regularly and reliably distinguishes between rain, snow, and mixed precipitation.
That’s where our observers come in. Humans are uniquely able to determine the precipitation phase (snow vs. rain vs. mixed) during a storm so our Mountain Rain or Snow observations are needed to validate the GPM precipitation phase data.
Into the Sierra Nevada
Prior research done by Mountain Rain or Snow found that GPM tends to favor rain more than snow, which led us to answer the following question: “Will storms with more moisture have better agreement between Mountain Rain or Snow observations and GPM PLP values?” We decided to look at Atmospheric River storms, which are storms that carry large amounts of moisture when compared to other storms in the western United States.
Now it was the matter of picking which Atmospheric River (AR) and non-Atmospheric River (non-AR) storms to analyze. We narrowed our search to the Sierra Nevada (a mountain range that mostly spans Eastern California and part of Western Nevada), particularly around the Lake Tahoe region. We decided to take an interesting route to comparing Mountain Rain or Snow observations and GPM PLP values in order to understand this mystery of agreement.
The animation – Grab your popcorn!
The challenge in comparing the agreement of Mountain Rain or Snow observations and GPM PLP values lies in how these data look in both space and time. Here, observations are displayed as points and GPM are displayed as grid cells. What better way to display this data through the progression of a storm than through an animation!
Atmospheric River storm on
December 12, 2021
Atmospheric River storm on
December 22, 2021
Non-Atmospheric River storm on
March 19, 2022
By matching the colors of these points and grids, we can see any first signs of agreement and disagreement between Mountain Rain or Snow observations and GPM PLP. The grid cells are on a gradient scale defined by the GPM PLP values, of which purple (0%) is likely snow and red (100%) is likely rain. The points are defined by observed precipitation type, similarly, purple (snow), green (mixed), and red (rain). For example, in the animation, if you see a purple dot on a purple grid, that’s agreement and that’s exactly what we want to be seeing. As you’ve probably guessed, if you were to see a red dot on a purple surface, that's disagreement.
Bonus: Another thing we’re able to notice through these animations is the spatial distribution of observations throughout the progression of a storm. In other words, we can see which areas are receiving the highest number of Mountain Rain or Snow observations and which areas are receiving the lowest. This information can then be used to create time series plots, which allow us to compare patterns of GPM PLP and Mountain Rain or Snow observations… but we’ll save this for later.
What are these results telling us?
When comparing the AR and non-AR animations, we notice a couple interesting things. One of the Atmospheric River storms used in this work was observed by GPM to be more rainy, and one was observed to be more snowy. Visually, we can see that the AR storm observed to be more rainy by GPM (December 22, 2021) performed well in terms of agreement, but the AR storm observed to be more snowy by GPM (December 12th, 2021) didn’t agree as much. For the non-AR event (March 19, 2022), we found this storm had varying agreement between MRoS observations and GPM.
Essentially, this work assesses the reliability of satellites using your observations. The GPM performance in differentiating rain, snow, and mixed precipitation varies regardless of air moisture content. As mentioned, more rainy AR storms had more agreement between the observations and PLP values, but there was no distinct determination between satellite performance and AR/non-AR storms. Further work may change what we’ve learned here. Be sure to stay up to date with our Mountain Rain or Snow report backs.
With your help, we will help NASA tune the GPM data processing to better map the true form of precipitation across the globe – even in places far from people but where snow may be critically important for ecosystems.
