About the Sasse Ridge Snowpack Model
The model is an attempt to predict recent additions to the snowpack on Sasse Ridge throughout the range of elevation, from the parking lot at 2350 feet to the summit of Jolly Mountain at 6443 feet. It’s quite simplistic I have to admit. The resulting graphs only cover the past week, but that is a period of great interest to a skier! There is a summary of the following info here.
Inputs are hourly data for temperature and precip from the Sasse Ridge SNOTEL site unless the precip gauge appears to be malfunctioning. In that case, SWE (snow water equivalent) data are used. The disadvantage of SWE data however is that if the temperature at the SNOTEL site above freezing, precip will not be detected properly and the possibility of snow higher up will be discounted. As a convenience, temperature, maximum wind speed and solar radiation are also displayed on one of the graphs.
The simplifying assumptions are:
- that during periods of precipitation, the air mass in the local area is relatively homogeneous and that the amount of precip is the same at all elevations covered by the model. (Of course this is not really true.)
- that during periods of precipitation, the temperature on Sasse Ridge drops roughly by the environmental lapse rate, calculated from the latest soundings at Quileute and predicted for Stampede Pass, available on the web from the NOAA Earth System Research Laboratory. Temperatures above 8000 feet are discounted.
- that precipitation will fall as snow and accumulate when the air temperature is below about 32 degrees (this value has been tweaked in the model);
- that an change in the precip counter that is followed in the next hour by an opposite but matching change is an aberration and represents no change;
- that the precip counter is incremental; the values should not decrease. Nonetheless, during periods without precip the baseline often wanders up and down. (More on sensor issues here.) For each data reporting period, the current model checks the values for the preceding 12 hours and uses the maximum if it is higher;
- that there have to be all sorts of hidden assumptions that are not accounted for.
The model currently figures new snow at roughly 1 inch of snow per tenth of an inch precip if the temperature is well below freezing, and it decreases that value according to an adjustable power function for temps near freezing. It’s a bit arbitrary but it helps smooth the transition between elevations on the chart, and it seems to be more realistic. Trying to get a more accurate estimate of new snow density is problematic and is discussed here.
How the model works:
It’s just a large Excel file with a number of macros controlled by a Windows script for automation. It calls up the Sasse Ridge SNOTEL data from the web and with the hourly temperature data from the site it predicts the temperature at 500 foot increments of elevation starting at the parking lot based on current lapse rate data it has collected from Stampede Pass. More on this here.
It uses the predicted temperature and the increase in precip since the last data period for each of these elevations to estimate the amount of new snow based on the assumptions above. If the precip values are suspect, such a decrease in the value, the value is corrected using ‘if-then‘ logic rules. The data for each hour reported from the site are similarly processed.
Excel then creates four different charts:
First is a bar graph with bars for the different elevations, from the parking lot to the summit. Each bar is made up of different colored segments that represent the snow that accumulated during two sequential data periods – usually 2 hours. (Graphs showing hourly data were too cumbersome.) The legend shows the date and time corresponding to each colored bar segment. The newest additions to the snowpack are on top. There is more detail about the graphs and how to interpret them here including how rain as well as periods without precip are depicted.
Note that the bar graph does not account for melting, settling or compression in the older layers of snow, nor does it account for wind transport, so the bars are not really virtual snowpits – in spite of the subtitle in the graph. However, it’s the easiest way to understand the chart I think.
Also note that wind transport in particular can make the conditions at higher elevations much different than one might expect from the graph.
Chart 2 depicts the rate of accumulation of new snow at 3 different elevations for the previous week. This can be another useful tool in accessing avalanche danger. More here. The lapse rate is now included for error checking, but it’s quite useful in it’s own right.
Chart 3 shows the total snow accumulated during each 2 hour interval for the past week. It also shows the solar radiation, and the maximum wind speed for what its worth. The solar radiation values are particularly useful in cross checking data. If the value is high, but the chart shows an inch of new snow during that period, something is suspect! More here.
Chart 4 is an attempt to predict the spring snowpack. More here.
Links to the graphs, which are generated automatically most mornings, are in the sidebar.
Friday, November 08, 2013
For summer and fall: during the summer the Sasse SNOTEL serves as a fire weather site, transmitting fire related data hourly, instead of every 6 hours as during winters. The summer charts linked in the side bar are posted when hourly fire weather data is available.
About (the guy who done wrote this stuff)