Report-back:
Observers send thousands of precipitation reports. Then what happens to them? 

At some point, the question may have crossed your mind of what happens to your precipitation observation after you press “Send It!”

To date, Mountain Rain or Snow observers have contributed over 25,000  observations in the 2023-2024 winter season, and over 65,000 observations since the project launched in 2020. Your simple submission allows our team and other scientists to better understand the rain-snow transition in both the short and long terms.  

Heat map showing phase observations to date (left) and bar graph of total observations over the course of the project (right). TRoS = Tahoe Rain or Snow. The number of observations has increased each year since the beginning of the project, and observations come from many areas across the United States.

But before we can use the data from Mountain Rain or Snow, we follow a multi-step process that enables scientists to answer research questions with the data. This is akin to the steps needed to process freshly harvested wheat berries into flour so that it can be made into bread. Instead of a baguette or a bagel, however, the final output is what we call “the processed dataset”. This season, we made huge advancements in this process.

When you send an observation, it arrives in our database with your report of the precipitation phase (rain, snow, or mixed), as well as the date, time, and location from which the observation was made. Each observation is first quality controlled to make sure there are no mistaken reports. 

The next step is the biggest part of the process: We pair each observation with complementary information from other data sources to provide scientific context for each report. This step is necessary to answer Mountain Rain or Snow’s research questions in the same way that adding water and yeast to flour are needed to make bread dough. For example, we match each observation with the elevation from where it was submitted so that we can learn about rain-snow lines in each storm.

We elaborate on the types of information that we pair with each observation next.

The types of information that we pair with each observation are organized in three categories: geographical data, observed meteorological data, and data products.

Here's an example of what we can do once the data have been quality controlled and processed with all the complementary information. 

One of the big questions that our team endeavors to answer is: What is the probability of snowfall in each region? To answer this, we run analyses on the processed dataset and create snowfall probability curves. Across all years of this project, we have seen changes to the snowfall probability curves as we got more observations. For example, see the yearly changes for four ecoregions. The grey lines are each season’s data (delineated by water year), and the black line indicates the cumulative snowfall probability along with the 50% snowfall probability air temperature. Learning about these differences is critical for many reasons, one being that the air temperature at the 50% snowfall probability varies across regions. With more observations we can fine tune this value, which helps with winter weather forecasting, emergency management, and overall recreational safety. Our optimized data processing pipeline increases the frequency and reproducibility of analyses like this. 

Here are the main takeaways: Your contribution to Mountain Rain or Snow is the crucial ingredient in the scientific recipe for informing decisions and increasing knowledge about winter weather, and our newly optimized data processing approach allows for more efficiency so that these data can be used by researchers much sooner.

Teaser for future work: The processed dataset will be available through our dashboard that is currently in development**. With this feature, you will soon be able to visualize, subset, and download data from all years of the project.

Here are the sources of the complementary or ancillary data that we mentioned above: 

• Geographical information: EPA ecoregions levels III and IV and state boundaries 

• Elevation: USGS 3DEP 10-meter digital elevation model (DEM)

• Observed air, dew, and wet bulb temperatures and relative humidity are collected at a 1º radius of an observation point from the following networks: Hydrometeorological Automated Data System (HADS), the Local Climatological Data (LCD), and the National Water and Climate Center’s (WCC) SNOTEL and Snolite data.

• Probability of liquid precipitation: NASA GPM IMERG, MERRA-2 (derived by using variables PRECSNOLAND and PRECTOTLAND), and NLDAS-2 (derived by using variables Snowf and Rainf).

The elevation data and observed meteorological data are inputs to model meteorological variables for an observation point. If you are interested in more details about our modeling approach, we expanded on our methods and protocol in Arienzo et al. (2021). The processed dataset includes the raw observation data and all the possible modeled meteorological variables for the observation. 

**We are partners with several operational forecast offices who are using the data in real time to regionally view what is falling from the sky right now. If you are interested in using our data before the dashboard launch, please contact any of our team members to get connected. If you are an operational forecaster and would like to receive access to the real-time data, please reach out to Sonia Nieminen (Sonia.Nieminen@dri.edu).  

Arienzo M.M., Collins, M., Jennings, K.S. (2021). Enhancing Engagement of Citizen Scientists to Monitor Precipitation Phase. Frontiers in Earth Science, 9. https://doi.org/10.3389/feart.2021.617594