In this example we used 'median' values to avoid the influence of anomalous events such as a big storm event. But you can also choose 'average' values for your analysis by selecting it on the control panel on the right side. The choice between using the median or average snow water equivalent (SWE) would depend on your specific priorities and goals. Here are some considerations to help you decide: Average (Mean) SWE: Comprehensive Overview: The average SWE can provide a broad overview of the snowpack's water content across the area. It considers all measurements equally, providing a general sense of the overall conditions. Influenced by Extremes: Be cautious that the average can be influenced by outliers or extreme values. If there are a few areas with significantly higher or lower SWE values, they could skew the average. General Trend: The average SWE can give you a sense of the typical conditions but may not capture variability or localized differences within the area. Median SWE: Representative Value: The median SWE represents the middle value in the dataset and is less influenced by extreme values or outliers. It provides insight into typical or "normal" conditions within the area. Less Skewed by Extremes: If the dataset contains outliers or areas with particularly high or low SWE values, the median will be less affected compared to the average. Focused on Typical Conditions: If you're interested in understanding typical conditions or what you might expect on average, the median can be a useful measure. Considerations for Skiing: Quality of Skiing: If you're looking for consistent and reliable skiing conditions, focusing on areas with a median SWE that aligns with typical or expected conditions may be beneficial. This approach can help you identify areas less likely to have extreme variations in snowpack. Variability: Keep in mind that snow conditions can vary within an area due to factors like elevation, aspect, recent weather patterns, and human-made snowmaking. While SWE provides valuable information, other factors can influence skiing conditions. Again, here is where using that pop-up chart outlined in steps 10 and 11 in the description above can be beneficial because you can access more granular data with regard to the stations in that area, parameters, meta data and reports. If you want elevation data for the area, you can toggle elevation on by going to 'Labels' < 'Station Labels'
In this example we used 'median' values to avoid the influence of anomalous events such as a big storm event.
But you can also choose 'average' values for your analysis by selecting it on the control panel on the right side.
The choice between using the median or average snow water equivalent (SWE) would depend on your specific priorities and goals.
Here are some considerations to help you decide:
Average (Mean) SWE: Comprehensive Overview: The average SWE can provide a broad overview of the snowpack's water content across the area. It considers all measurements equally, providing a general sense of the overall conditions. Influenced by Extremes: Be cautious that the average can be influenced by outliers or extreme values. If there are a few areas with significantly higher or lower SWE values, they could skew the average. General Trend: The average SWE can give you a sense of the typical conditions but may not capture variability or localized differences within the area.
Median SWE: Representative Value: The median SWE represents the middle value in the dataset and is less influenced by extreme values or outliers. It provides insight into typical or "normal" conditions within the area. Less Skewed by Extremes: If the dataset contains outliers or areas with particularly high or low SWE values, the median will be less affected compared to the average. Focused on Typical Conditions: If you're interested in understanding typical conditions or what you might expect on average, the median can be a useful measure.
Considerations for Skiing: Quality of Skiing: If you're looking for consistent and reliable skiing conditions, focusing on areas with a median SWE that aligns with typical or expected conditions may be beneficial. This approach can help you identify areas less likely to have extreme variations in snowpack.
Variability: Keep in mind that snow conditions can vary within an area due to factors like elevation, aspect, recent weather patterns, and human-made snowmaking. While SWE provides valuable information, other factors can influence skiing conditions. Again, here is where using that pop-up chart outlined in steps 10 and 11 in the description above can be beneficial because you can access more granular data with regard to the stations in that area, parameters, meta data and reports.
If you want elevation data for the area, you can toggle elevation on by going to 'Labels' < 'Station Labels'