|Figure 1 SREF ensemble forecast of snow fall in Norman, OK from Wednesday 2011-02-08 21 UTC. The snow fall forecast was based on a snow ratio technique dependent on surface temperature. The most conservative forecast in this picture was 8". The highlighted member (bright white) comes from the KF version of the WRF model using the ARW core with a snow depth of nearly 15". See http://www.wrf-model.org/users/users.php for details.|
|The sharp arctic front depicted by the 850 mb frontogenesis in this SPC analysis at 02 UTC 09 Feb 2011. I'll put this down as one of the all-star fronts.|
|Surface plot courtesy of NCAR/RAP for 2233 UTC 08 Feb 2011. The front features a contrast from 82 deg F at Pecos, TX to 9 deg F in Clovis, NM.|
|Figure 5 Like figure 1 except for QPF. The highlighted SREF member is the ARW KF version of the WRF. The bar plot at the bottom represent hourly accumulations.|
But the snow accumulation is incredibly dependent on the forecasted density of the snowfall. No model explicitly predicts snow density and so we're dependent on empirical relationships. The technique used to come up with the snowfall forecasts in the figure 1 assumes that the snow density decreases as the surface temperature decreases. See below the relationship between both parameters and you see what I mean. This shows the snow ratio forecast exceeding 25:1 after 09 UTC, the highest values of all the techniques I present (see figure 6). Basing the snow ratio from the surface temperature is often not a reasonable strategy since snow flakes, and their density, is dependent on the temperature and humidity where they form. However if surface temperature approaches freezing then the snow pack may morph into a higher density. This case is not a candidate for warm surface temperatures.
Instead of using the 2m temperature, perhaps the maximum temperature in the vertical profile (MaxTemp) may provide a more realistic snow ratio. The snow ratio is inversely proportional to the MaxTemp. The thinking behind the MaxTemp technique is that snow flakes entering into the warmest layer could be subjected to riming in which cloud liquid drops freeze directly onto the flakes helping to reduce interstitial spaces between ice and thereby increasing snow density and decreasing the snow to liquid ratio. If we assume that MaxTemp is saturated then it seems reasonable that riming is more likely if MaxTemp is warmer because cloud liquid water content would also be higher. For this storm, the MaxTemp was forecasted to drop as the cold air deepened and thus the forecasted snow ratios for each SREF member increased with time. The same SREF member we used above in figure 1 shows a lower snow ratio using the MaxTemp technique and the subsequent forecasted snowfall came out to nearly one foot, less than the 15" using the surface temperature. Even though the snow ratio is lower, it was still considerably higher than the mean snow ratio for central Oklahoma.
|Figure 7 Like figure 6 except the snow accumulation and snow ratios are from the MaxTemp in profile snow ratio technique. The yellow contours are temperature plotted as a function of time and height.|
Considering the same SREF ARW WRF member as before, we find that the overall snowfall forecast decreased down to 9" in figure 8 when applying the Zone omega technique. The snow ratio, in blue, was quite volatile as the level of peak ascent fell in and out of the dendrite growth zone (yellow contours). Note that early on, the peak vertical motion was quite low resulting in a low snow ratio and then there was a relatively narrow window where the majority of ascent centered on the dendrite growth zone and the snow ratio spiked to 30:1 at 12-13 UTC on the 9th. The greatest snowfall rate is more a function of the Zone Omega technique's huge snow ratios than the actual SWE rate visualized in figure 5.
Just after the snow event ended around noon, I took a core sample of the snow using the standard 10" deep clearview rain gauge that is the standard for the COCORAHS observing network and melted it to get 0.37" of SWE. Another similar gauge left open in the snow storm resulted in 0.41" of SWE. Our SWE was below the featured SREF member (figures 5-8) but it was higher than the member with the minimum QPF. So in short, our SWE was anticipated as a possibility according to the 21 UTC SREF model run.
Given a snow depth of only 2.7", my snow ratio was a paltry 6.6:1. This value was well below even the most dense snow ratio technique applied above. My snow ratio value was far below climatology as determined by Baxter et al. (2005) while the median value is near the median climatological value. But all these values were well below the forecasted values by all SREF members, especially for those snow ratio techniques using temperature. But even the Zone Omega technique overestimated the snow ratios during the forecasted maximum snowfall rates centered around 12 UTC. But, earlier in the forecasted snowfall, the Zone Omega technique forecasted snow ratios < 10:1 when the maximum ascent didn't reach the dendrite production layer.
The measurements I mentioned so far represent the sum total of what happened in the storm. But there's more complexity to the snow crystal types that yielded a broader variety of snow ratios. I took a picture of our snow core balancing on the top of a ruler (official NWS ruler) and it showed some interesting changes in crystal types (fig. 9). For instance, the lower third of the core consisted of tightly packed small crystals that appeared to be small graupel. Above that layer, the middle third appeared to be small aggregates of small dendrites, still appearing quite dense. Luckily, I measured the depth of these two layers before the third one fell in the late morning and got 2.2" of snow depth with 0.37" of SWE from the gauge and 0.31" from a core sample. Just taking the 0.37" from the gauge, I got a snow ratio of only 5.9:1. This layer of snow was dense, acting almost like machine-made snow but it was definitely not wet. During the late morning, a final round of snow fell in the form of some of the biggest dendrites I've seen (see fig 10). They were bundled up in large aggregates. About 0.5" of snow accumulated with this last round and I got about .03 to .04" of SWE. This ratio was a considerably larger 16:1, and quite expected for the flake type I saw.
|Figure 9 A picture of a snow core sample taken at 18 UTC 09 Feb 2011 showing a bottom layer of graupel, a middle layer of small dendrites and an upper layer of large dendrites.|
|Figure 10 A closeup picture of the upper layer of large dendrites that fell in the late morning of 09 Feb 2011 in east Norman. Photo courtesy of Daphne LaDue.|
|figure 10. A sounding display for Norman taken 09 Feb 2011, 12 UTC.|
All of the snow ratio techniques that I discussed do little to consider the effects of solar radiation, temperature and wind upon the density of the snow pack. I decided to measure the snow ratio after the end of the snow events, the first measurement coming at 13 UTC and the final one at 18 UTC after the large dendrites finished falling. Temperatures near freezing could accelerate crystalline metamorphasis into denser forms and the snowpack density could increase. There was some wind which could've broken the crystals upon landing and increased the density. Roebber et al. (2003) described in detail these time dependent processes that alter the density of freshly fallen snow. However, the temperatures for this event were less than 15 deg F and so I was not concerned about melting. In addition, the snow core pictures didn't support the contention that the wind was strong enough to break a majority of crystals. There was some natural settling to consider that may bring down the snow ratio forecasts a bit. BUFKIT attempts to account for settling by applying an exponential decay function to the forecast snow depth. The effects of this function can be seen in figures 6-8. Most of my measurement times were near the forecasted peak snowdepth and so this effect wasn't very significant.
The Zone Omega technique may have won out in this case but I wouldn't vouche for its continued relative success for every event. This technique doesn't account for errors in the vertical motion field, or other processes that include riming, and changes to snowpack density due to settling, wind, and melting. In fact, for a well known New York City snowstorm on 26 January 2011 where 19" of snow fell in Central Park, the Zone Omega technique forecasted snow ratios that were far too high than observed. In that case, relatively maximum elevated temperatures were closer to freezing and that layer featured a very strong flow ascending over the frontal surface ahead of strong low-level cyclogenesis. In that case, the max temp in profile technique beat all others perhaps because that technique applies well when significant riming, and warm ice crystal production occurs in the face of strong saturated ascent at relatively warm temperatures.
Our observed SWE was actually contained within the envelope of possible QPF based on the 21 UTC SREF.
The surface temperature and MaxTemp snow ratio techniques bombed terribly because the temperatures were cold. Clearly these techniques failed to represent the mechanisms influencing the density of falling snow. Dense snow can occur at very cold temperatures, surface or aloft.
The ZoneOmega technique performed the best of the three techniques but still far overestimated the snowfall. Given the small snow ratios observed, and the compact crystal nature seen in the snow core, I suspect that much of the precipitation was forming below the dendrite production zone perhaps because the vertical motion field was too shallow. Only when the temperatures cooled did the shallow vertical motion extend into the dendrite production zone. Still, this technique did better than the others. Don't expect this to be true all the time, however.
Baxter, A. A., C. E. Graves, J. T. Moore, 2005: A Climatology of Snow-to-Liquid Ratio for the Contiguous United States. Wea. Forecasting, 20, 729–744.
Cobb, D. K., and J. Waldstreicher, 2005: A simple physically-based snowfall algorithm. Preprints, 21st Conf. on Weather Analysis and Forecasting, Washington D.C., American Meteorological Society
Roebber, P. J., S. L. Bruening, D. M. Schultz, J. V. Cortinas, 2003: Improving Snowfall Forecasting by Diagnosing Snow Density. Wea. Forecasting, 18, 264–287.