Brian Miretzky 1

A Study on Snow Density Variations at Different Elevations

and the Related Consequences; Especially to Forecasting.

Brian J. Miretzky

Senior Undergrad at the University of Wisconsin- Madison

ABSTRACT

With observations becoming more common and forecasting becoming more accurate snow density research has increased. These new observations can be correlated with current knowledge to develop reasons for snow density variations. Currently there is still no specific consensus on what are the important characteristics in snow density variations. There are many points of general agreement such as that temperature and relative humidity are key factors. How to incorporate these into snowfall prediction is the key. Once there is more substantiated knowledge, more accurate forecasts can be made. Finally, the last in the chain of events is that society can plan accordingly to this new data. Better prediction means less work, less time, and less money spent. This study attempts to determine if elevation is an important characteristic in snow density variations. It also looks at computer models and how snow density is used in conjunction with these models to make snowfall projections. The results seem to show elevation is not an important characteristic. The results also show that model accuracy is more important than snow density variations when using models to make snowfall projections.

1. Introduction volume of the sample. Snow densities are typically on an order of 70 to 150 kg The density of snow is a m-3, but can be lower or higher in some very important topic in winter weather. cases. This is because the density of snow The fact that snow densities can helps determine characteristics of a vary makes them one of the most given snowfall. For example, if there are important characteristics of snowfall two 2 inch snowfalls and one of them is prediction. This means that each not as dense, the less dense snowfall will individual snowfall has a different be more likely to blow and drift. This density, and thus different water could then possibly cause whiteout equivalence. The American conditions, which would be hazardous to Meteorological Society glossary defines driving. The glossary of meteorology by water equivalence as the depth of water the American Meteorological Society that would result from the melting of the defines snow density as the ratio of the snowpack or snow sample. Since the volume of meltwater that can be derived density of water can be assumed to be a from a sample of snow to the original constant 1000 kg m -3, the depth of the Brian Miretzky 2 water that results from melting mainly as a simple ratio. SLR normally snowpack is much less that the depth of assumes a specific snow density so that a the snowpack itself. This shows an certain ratio may be applied to given interesting correlation between situation. SWE can be seen as a precipitation in the summer and winter. verification of SLR. If an SLR for a In the summer, a big rainstorm could given location was assumed to be give 2 inch precipitation totals. If the 2 correct, then the SWE for that snow inches of rain were instead to fall as should verify it. The assumption in the snow, there would be approximately 20 last situation is that there is no snow on inches of snow if an average snow the ground. If there was already snow density of 100 kg m -3 were assumed. present then the measurement for SWE Since most snowstorms do not come would be a combination of the new snow close to this snowfall amount, we can and snow that already fallen. These see that usually the amount of liquid different snowfalls would have different present is much less in the winter. snow densities and thus a SLR could not In general, forecasting be applied. SLR is used more for precipitation is not an exact science. prediction and verification of individual Models are used as a tool to help snowfalls [ Baxter et al., 2004]. forecasters make decisions about how Conversely SWE is used more for much precipitation to forecast. These nowcasting and daily measurements of models output Quantitative Precipitation the snowpack [ Rasmussen et al., 2003]. Forecasts (QPFs), which give an amount Nowcasting is the same as forecasting of precipitation expected for a given but provides information for only a small location. QPFs are getting better with amount of time ahead of the current time, but are still by no means absolutely time. Snow density can then be correct. Even if they are approximately calculated from SWE by dividing it by correct, snow density variations present the snow depth [ Schmidlin et al., 1995]. problems when trying to estimate a SWE is also usually measured daily by snow-to-liquid-equivalent ratio (SLR) different organizations including the [Baxter et al., 2004]. For example, United States National Weather Service assume a QPF of 1 inch. If temperatures and the U.S. National Resources were cold enough for snow then snow Conservation Service (NRCS). The density will be the determining factor in United States National Climate Data how much snow is received. If the snow Center does automated control on the were very dense, there might only be 6 National Weather Service data inches of snow. But if the snow was very [Schmidlin et al., 1995]. The NRCS uses light, there might be as much as 14 a network called SNOTEL, which stands inches of snow. Thus making a good for snowpack telemetry, to make its forecast is just one part of predicting observations. The observations are snowfall totals. needed because SWE is important for Another concern in relation to many reasons. Knowledge of SWE is snow density is the snow water helpful in snow removal, potential equivalent (SWE). This refers to the runoff, hydrological and engineering water equivalent of snow on the ground applications, and climate change studies [Schmidlin et al., 1995]. SWE is [Schmidlin et al., 1995]. different from SLR in that SLR is used Brian Miretzky 3

Snow density was long neglected in Steamboat Springs, Colorado as a as a topic of research because a ten-to- time to study the effects of elevation on one ratio of snow to liquid water was snow density. The goal then was to assumed. This assumption derived from develop a dataset to test the hypothesis a late nineteenth century Canadian study that elevation is a defining characteristic and was implemented in the United in snow density. Environmental States around the same time [ Roebber et variables must also be looked at to make al., 2003]. Even back then this ratio was accurate determinations of why a certain warned to be an average and not true for amount of snow fell. In addition data every case [ Roebber et al., 2003]. A few from a few other selected snow events studies were done during the mid are presented to further test the 1900’s, but there was another lull in hypothesis. Also, measurements of research until recently. One reason for accumulated snow were taken at SPL to snow density not being studied much in see how density differs after a snow the past is that until the 70’s Doppler event is over and if it leads to any radars and other types of observation additional conclusions about the density equipment were not in place. Now that of the actual snow event. A final related there is a comprehensive observation hypothesis is that snow density network and ways to verify these variations are the most important factor observations using radar and satellite, in making snowfall forecasts from model more comprehensive studies can be projections. done. Another reason for snow density not being studied much is that 2. Methodology forecasting has only currently become a The testing of the hypotheses bigger topic because of better modeling was mainly done at the Storm Peak and computer technology. Now that Laboratory in Steamboat Springs, forecasting has improved researchers are Colorado from March 11 th thru the 17 th . trying to understand the underlying Storm Peak Laboratory is located near errors involved. Thus snow density is the top of Mt. Werner at 10,500 feet or now being studied because it is an 3200 meters. Storm Peak Laboratory is important characteristic to making better run by Dr. Randolph Borys and is a snowfall forecasts. subsidiary of the Desert Research The importance of snow density, Institute. The trip to Storm Peak as previously described, and the lack of Laboratory (SPL) was funded by the specific knowledge of what changes it University of Wisconsin and was led by are the main motivations for this work. Dr. Greg Tripoli. SPL has an abundance Especially with the lack of many studies of equipment to use in measuring on how elevation changes snow density. different types of winter weather and Thus, this paper uses the author’s thus was a good place for my weeklong stay at Storm Peak Laboratory experiment. (SPL) near the summit of Mount Werner Brian Miretzky 4

Figure 1. Trail map of Steamboat Springs Ski Resort. Locations denoted are SPL (black arrow at top), Bar-UE Pumphouse (red arrow in the middle of the mountain to the left of the red sign), and Top of the Gondola (purple circle and arrow on the right middle side of the picture).

Brian Miretzky 5

The set-up for the experiment boards were flat and white so that they was as follows. Four locations along the were suitable for snow collection. To windward side of the mountain (where make sure snow was not blown onto the ski area is located) were selected as these boards, the immediate surrounding suitable sites for measuring snow area was cleared of snow so that the density. The locations were the base of boards were located on mini-plateaus. the mountain or ski resort (6,900ft., The boards were kept in place by a long 2103m.), the top of the Gondola wooden stick which ran through a hole (9,100ft., 2773.7m), the BAR-UE in the board and into the ground. The pumphouse(9,160ft., 2792m.), and the three locations I manned were picked SPL lab(10,500ft., 3200m). The SPL lab because mesonet data, which recorded location was only used for measuring temperature and relative humidity, was accumulated snow since no more boards available for these sites. Measurements were available for use. To measure new of new snow depth in centimeters and snow two other locations at the top of snow water equivalence in millimeters the mountain were used. The locations were taken in order to calculate a new were the actual Storm Peak (10,359ft., snow density value (kg m -3). New snow 3157m.) and the Mt. Werner summit density represents a ratio of snow water (10,569ft., 3221.4m). These locations equivalency to new snow depth were operated by a fellow colleague [Schmidlin et al., 1995]. Depth and SWE Andy Thut. At both the bottom three measurements were completed with a locations and the two locations manned Snowmetrics T1 sampling tube and by Andy Thut flat plywood boards were hanging spring scale. placed on top of the snowpack. These

Figure 2. Snow Density sampling kit provided by SPL. Includes scale, sampling tube, and shovel.

Using these measurements a simple densities. This calculation worked calculation of ((SWE/new snow depth) * because the spring scale converts the 1000) was used to derive new snow weight of the sample into water Brian Miretzky 6 equivalence. Once a measurement was to get back to the lab a lift that closed at taken the board was cleared so that old 3:15 had to be taken up. Thus for the snow did not mix with any future locations not at the top the boards could accumulating snow. Andy’s calculations not be placed until March 12 th . The top were a little different because this spring locations could be set up on the 11 th scale was not used. Instead, he used the because a snowmobile could be used to volume and mass to calculate density. get to these locations. By the time the Accumulated snow measurements were boards were set up on the 12 th it was too also taken at specific times. The same late to take any measurements that day. amount was taken at each station so that Finally, on March 13 th the day after a big the densities could be compared exactly. snow the boards could be used. This did One other snow events were not work out though because the analyzed. The other event was a three portable scale was not working. This day event over the central Plain states may have been due to the fact it got and Ohio Valley. This event occurred covered by snow during a fall on the March 19 th thru 21 st of 2006. Data was slopes. The only other day when snow taken from the Interactive Weather had accumulated at the three locations Information Network (IWIN) of the not at the top was March 16 th , and on National Weather Service. Again snow that day the base measurement had been depth and water equivalence were used tampered with. On March 17 th , as the variables to calculate snow depth. accumulated snow was measured at the The National Weather Service uses three locations I operated. To gauges to measure snow accumulation supplement the data of these locations and water content. These gauges are the two locations manned by Andy Thut very similar to what I used in my study. were used. For these locations reliable As always there is a possibility the data was available for the 13 th and 17 th gauges are individually biased, but of March. The following is a table that assuming these biases are small the includes the SPL data plus the other measurements are very good for snow event. In addition figures depicting comparison with my measurements in actual and predicted precipitation for Colorado. March 12 th -13 th , 2006 are included for comparison. 4. Results The big problem when running an experiment that relies on observations is that there might be no observations to take. This is especially true on studies that observe over a small time scale. Thus when the group arrived on March 11 th is was already too late in the day to begin observations even though it was snowing. It was too late because in order

Brian Miretzky 7

Table 1(a) – New Snow measurements

Place Eleva- Date/ Snow Water Snow Temp. Relative

tion Time Depth Equivalence density (°C) Humidity

(m) (mm) (mm) (kg m -3) (percent)

Storm 3157 March 352.4 NA 79.1 Avg. Avg. 92

Peak 13 th , over-

9am night

(MT) temp.

-16

Mt. 3221.4 Same Missing NA 141.1 Same Same

Werner as data as

above above

Storm 3157 13 th at 38.1 NA 40.7 Avg. Avg. 85

Peak 1:15 temp.

pm since

(MT) 9am

-13.5

Mt. 3221.4 Same 33.8 NA 40.4 Same Same

Werner as

above

Storm 3157 13 th at 46 NA 29.6 Avg. same

Peak 4:30 temp.

pm since Brian Miretzky 8

(MT) 1pm

-14

Mt. 3221.4 Same 38.1 NA 52.5 Same Same

Werner as

above

BAR-UE 2792 16 th at 16 3.5 218.75 Avg. Avg.

9am temp from day

(MT) from before

day when

before snowed

-10.3 95

Top of 2773.7 16 th at 42 10.5 250 Same Same

Gondola 11am

(MT)

Grand 567 Sum 548.6 54.1 98.6 NA NA

Island, of

NE Daily

totals

for

March

19 th -

21 st ,

2006 Brian Miretzky 9

Hastings, 588 Same 538.5 46.2 85.8 NA NA

NE

Kearney, 663 Same 428.2 47 109.8 NA NA

NE

Cincinnat 168.8 Daily 71.1 3.1 43.6 NA NA i, Ohio total

for

March

21 st ,

2006

Dayton, 239 Same 20.3 2 98.5 NA NA

Ohio

Moline, 178 Same 66 1 15.2 NA NA

IL

Cedar 222.8 Same 25.4 1 39.4 NA NA

Rapids,

IA

Spring- 182.9 Same 139.7 7.9 56.5 Avg. NA field, IL -1.6

Peoria, IL 209.1 Same 127 10.2 80.3 Avg. NA

-0.5

Table 1(b) Accumulated snow measurements (Same variables) Brian Miretzky 10

BAR- 2792 March 76.2 12 157.5 NA NA

UE 16 th ,

2006

Top of 2773.2 Same 76.2 13 170.6 NA NA

Gondola

Base 2103 Same 76.2 14 183.7 NA NA

BAR- 2792 March 76.2 13 170.6 NA NA

UE 17 th ,

2006

Top of 2773.2 Same 76.2 13 170.6 NA NA

Gondola

Storm 3157 Same 304.8 48 157.5 NA NA

Peak

Mt. 3221.4 Same 304.8 56 183.7 NA NA

Werner

SPL 3200 Same 304.8 70 229 NA NA

Brian Miretzky 11

Figure 3. 24 hour accumulated precipitation from March 12 th , 2006 at 12Z to March 13 th , 2006 at 12Z. Map courtesy of the Climate Prediction Center.

Brian Miretzky 12

Figure 4. 24 hour forecasted precipitation by the NAM model 12.2km grid (known formerly as the ETA model) valid at 12Z on the 13 th of March, 2006.

Figure 5. 24 hour forecasted precipitation by the GFS model (formerly known as the AVN model) on a 40.6km grid valid at 12Z on the 13 th of March, 2006.

Brian Miretzky 13

Figure 6. 24 hour forecasted precipitation by the NGM model on a 40.6km grid valid at 12Z on the 13 th of March, 2006.

5. Discussion snow less dense since water is heavier The results presented in Tables then snow. Elevation is possibly a cause 1a. and 1b. show some interesting for the high densities seen on the 16 th at things. The first thing to look at is the BAR-UE Pumphouse and the top of the direct relationship between snow density Gondola. One reason besides elevation and elevation and if there is one present. that the densities were especially high Looking at the data from the SPL trip was probably the fact that the snow was first it is hard to see any correlation from the evening before. This extra time between elevation and density. Two of allowed for settling and compaction. But the Mount Werner observations do not there is also a slight density change seem to fit the rest of the data. The two between the two sites. The difference are the measurements taken at 9am and between the two sites and the fact that 4:30pm on March 13 th . The 9am both sites had much greater densities measurement of snow density is then the mountain top seems to imply substantially larger then the density that snow density decreases with height. observation taken at the same time at The data from the March 19 th Storm Peak, which is relatively close in thru 21 st snow event does not really both horizontal distance and vertical correlate with the thought that snow elevation to the Mt. Werner site. Also, density decreases with elevation. the 4:30 density measurement on Mt. Temperatures and relative humidity’s are Werner was up from the previous not available for this event. Thus there is measurement, which differs again from no outside information to help explain Storm Peak. At Storm Peak the density any differences. Nebraska received the of the new snow was lower then the most snowfall from this event. Both previous measurement. These stations do Grand Island, NE and Hastings, NE not differ that much, so both Mt. Werner received over 20 inches while Kearney, measurements are questionable. The NE received 16.7 inches. These were only thing that can be inferred from both some of the highest totals recorded in of these places on the 13 th is that relative Nebraska. These three cities are very humidity and temperature do seem to close in proximity. Kearney is the have a pronounced effect on snow farthest away and is about 50 miles from density. This is seen from the Storm both Hastings and Grand Island. Peak measurements. When the relative Kearney is to the west and has the humidity decreased from an average of highest elevation of the three cities. In 92 percent to 86 percent the snow this storm the snow that fell in Kearney density decreased. This relative humidity had a higher density then the other two decrease was probably associated in places. This seems to contradict that some way with the temperature increase. density decreases with height. Because there was less moisture there Conversely Hastings which has a was less perceptible water in the second slightly higher elevation then Grand snow of the day. Less water will make a Island has a lower density then the Brian Miretzky 14

Grand Island snowfall. These three cities grid spacing meaning that it has a best show that elevation was probably not resolution of the three. The other two very important in determining the models have grid spacing more then density of the snowfall in this storm. double the NAM. Thus, the NAM Also, when comparing other places that should normally forecast precipitation are close in this storm like Cincinnati events for small areas better then the and Dayton, Moline and Cedar Rapids, other two models. This is true for the and Springfield and Peoria, the place period of time being discussed. Over the with the higher elevation always had a United States the NAM forecasted higher snow density. precipitation map correlates the most Another way to try to make some with actual precipitation map. The NGM deduction on how elevation affects snow is the worst forecast especially over the density was done by using accumulated mountain’s west side where snow. The first thing that stands out is mountainous terrain creates small that the accumulated snow is generally pockets of oragraphically enhanced denser. This is likely due to compaction precipitation. These figures show that and other ground processes. Also, the even though snow densities are still not data from March 16 th shows a clear exactly known for different locations distinction between height and density. models first must make accurate QPF The March 16 th data shows a decrease in forecasts for Snow to Liquid ratios to density with height. There is again even be correctly used. Thus snow opposing data this time from March 17 th , density is important in translating which except for the Storm Peak QPF’S, but not very important in the measurement shows an increase in snow model’s correctness for a precipitation density with height. Thus, the results are forecast. inconclusive on the effect of elevation Another model that is not shown on snow density. in any figure is the University of Snow density variations also Wisconsin - Nonhydrostatic Modeling have effects on how to use model System (UW-NMS) model developed by projections to predict snowfall. This idea Dr. Greg Tripoli. For Steamboat the can be looked at in relation to the snow model was run over a higher resolution that fell at the Steamboat ski area on the to see the topography present. Unlike night of March 12 th . Figures 3 thru 6 most models, the UW-NMS uses a help to show how models compare in conversion to convert QPF to a snow QPF forecasts. Figure 3 is a map of the amount. This conversion calculates a actual precipitation that occurred during snow ratio based on a few variables like the 24 hour period of March 12 th at 12Z air temperature and temperature of the to March 13 th at 0Z. Figure 4 shows how soil. For the night of March 12 th , the much precipitation the North American model did very well in predicting the Model (NAM) forecasted for the same amount of snow. There was period. Figure 5 is the same but for the approximately 15 inches of snow Global Forecast System (GFS) model overnight at the Storm Peak Lab. The and figure 6 is the Nested Grid Model UW-NMS model predicted 14 inches. (NGM) model. All three models do not The fact that it is a mesoscale model and exactly predict the actual amounts of was done over small grid spacing helped precipitation. The NAM has the smallest it predict the snowfall well (Model Brian Miretzky 15

Information courtesy of Dr. Greg environmental conditions and thus the Tripoli). final crystal is a combination of types This case study does not present [Roebber et al., 2003]. Crystal size enough data and significant results to be depends on how much time spent in the either correlated with or contradicted cloud and the degree of supersaturation with previous work. Other studies [Roebber et al., 2003]. Some crystals though can be compared and contrasted will grow relative to their neighbors to see what current research is leading to many small particles of low concluding. The trend of recent research density being swept out of the cloud has been to move away from the old [Roebber et al., 2003]. If an ice crystal average ratio of ten-to-one and come up falls through a cloud of supercooled with a better standard. In order to do water droplets, on the other hand, this this, researchers first have had to will lead to rimed crystals (graupel) and determine what causes fluctuations in very high snow densities [ Roebber et al., snow density. The only way to do this 2003]. After ice crystals leave the cloud has been to go out and use observations sublimation and melting occur over short to support new conclusions. In these distances [ Roebber et al., 2003]. Low case studies many variables were level temperature and relative humidity identified as playing a key role in snow are central to these processes [ Roebber density. The limited results from this et al., 2003]. Finally, once on the ground study did not delve into the large compaction can occur. Wind can move amounts of variables shown to be ice crystals around causing surface involved in snow density. In such a compaction, which will increase snow limited study it would have been hard to density [ Roebber et al., 2003]. Also, the prove which factor stood out in weight of the snowfall can further determining snow density. compress the snowpack [ Roebber et al., One study includes a summary of 2003]. It has been found that there are the processes that influence snow ways to try to eliminate these last density. The processes are in-cloud influences by shading the observations processes that are associated with the from the wind and taking the shape and size of the ice crystals, measurements quickly so as not to allow subcloud processes that modify the ice for much compaction [ Wetzel et al., crystal as it falls, ground level 2004]. This was done in Steamboat by compaction due to prevailing weather putting the sites in places shielded from conditions , and snowpack the direct wind. But afterwards it was metamorphism [ Roebber et al., 2003]. realized that the sites were not shielded Inside the cloud the shape of the ice enough on extremely windy days like crystal is determined by the surrounding March 15 th when winds where gusting to air temperature and the degree of over 50mph at the top of the mountain. supersaturation with respect to ice and Through all the case liquid [ Roebber et al., 2003]. The types studies, temperature and relative of crystals that result are plates, humidity seem to be the dominate dendrites, needles, columns, and others. factors influencing snow density. The Each crystal shape has a different first and major emphasis of snow density density, but as ice crystals fall through research has been to take observations of the cloud they experience different certain places over a period of time to Brian Miretzky 16 determine the characteristic snow put to use but not exactly in the way the density of that place. For example a Baxter study proposed. One recent study study by Judson and Doesken [2000] by Roebber and colleagues [2004] used attempted to determine the density of data from many events but not long term freshly fallen snow in the central Rocky climatology. This study used the data to Mountains. Their study found that snow create an Artificial Neural Network densities were lower at lower elevations. (ANN), which predicts the expected Another study by Wetzel et al. [2004] snow density when separated into three also looked at snow density in this area, classes: light, average, and heavy. This but their goals were different. Judson network uses technology that “learns”, and Doesken [2000] only wanted to sort of like humans do, and adapts to determine snow density. Wetzel et al. data that it does not immediately [2004] were bolder and looked at how recognize. This network has proven to snow density fits into the grand scheme beat both climatology’s and the National of snowfall prediction in mountainous Weather Service’s “new snowfall to regions. But during the course of the estimated meltwater conversion” table. study, Wetzel et al. found that snow As shown the ANN is an advancement densities are lowest at the highest over others, but still has errors of its own elevations. This directly contradicts the and is not perfect. In an even more Judson and Doesken [2000] study. But recent paper that discusses the data from the Judson and Doesken [2000] study the ANN, snow ratio was found to was done over a larger spatial scale. increase as the liquid equivalent Moreover, observed density differences decreases [ Ware , 2006]. Some other over a large region should not be empirical techniques exist to diagnose analyzed in the same manner as small snowfall in the absence of explicit snow scale studies because of differing density forecasts, but there is an influence related to synoptic and argument over whether these techniques mesoscale (storm system origin) have any value in real world applications conditions, as well as microscale [Roebber et al., 2003]. Empirical influences (complex topography). techniques use large scale observations Another study by Baxter et al. like where the flow is coming from and [2004] looked at thirty year climatology where the greatest rising motions are to of snow to liquid equivalent ratio in the predict the amount of snow expected. contiguous United States. This goal of The techniques used by the Baxter and this study was to create a better set Roebber studies take a historical standard for different locations using approach to looking at data and propose climatology. The study developed mathematical solutions using the data to varying standards and proposed the problem of predicting snow density. explanations for these standards based Their solutions differ from each other, on the kind of conditions usually but the studies arrive at these solutions experienced in a certain place. The from similar viewpoints. These two overall conclusion though, was that studies differ from the direct climatology while a good standard could observational approach, which was be put to even better use in creating an shown by the Judson and Doesken and algorithm for snow to liquid Wetzel studies. These studies are equivalence. This idea has already been beneficial because they can directly Brian Miretzky 17 show variations specific to an event, but Snow density of accumulated snow is are hard to really apply on a large scale also needed to parameterize the melting like the Baxter and Roebber studies process in hydrological computations attempt to do. Both types of studies have (Brasnett, 1999). Also, future valid results and use for the future. advancements will allow operational meteorologists to better predict snowfall 6. Conclusion and to snow hydrologists in figuring out Snow density is a topic in water amounts present in a certain area. meteorology that is still yet to be fully Better prediction will allow for money understood. Many of the mechanisms by and lives to be saved [ Judson and which snow density varies have been Doesken, 2000]. Snow density has found, but the way these mechanisms become a bigger topic recently and if cause the variations is still up for debate. there is continued research, much more My work included only a small amount will soon be known about a key factor in of research. It did not really find a the lives of many people around the correlation between elevation and snow globe during the winter months. density. Since there were not many observations no definite conclusions can Works Cited be made, but it seems that elevation is 1. Baxter, M.A., C.E. Graves, and not an important characteristic in J.T. Moore (2004), A determining snow density. Also, this Climatology of Snow-to-Liquid study showed that snow density while Ratio for the Contiguous United important for translating QPF’S, is not States, Weather and Forecasting, as important as the model’s correctness 20, (5), 729-744, of a precipitation forecast for snowfall doi:10.1175/WAF856.1. projections. But if others continue this 2. Brasnett, Bruce (1999), a Global type of work, in the future different Analysis of Snow Depth for studies may be combined into a larger Numerical Weather Prediction, more comprehensive study. This last Journal of Applied Meteorology, statement has yet to be accomplished. In 38, (6), 726-740. reality, the only way to get a real data 3. Judson, A., and N. Doesken comparison for a large scale conclusion (2000), Density of Freshly Fallen is to combine research from different Snow in the Central Rocky places. This paper is an example of how Mountains, Bulletin of the to use research in the context of other American Meteorological research to compare and contrast results. Society, 81, (7), 1577-1587, If more results are compared then more doi:10.1175/1520- conclusions can be drawn. Once better 0477(2000)081<1577. knowledge of the spatial and temporal 4. Rasmussen, Roy, M. Dixon, S. characteristics of snow density and its Vasiloff, F. Hage, S. Knight, J. derivatives, like snow water equivalence Vivekanandan, M. Xu (2003), and snow depth, becomes known then Snow Nowcasting Using a Real- further advances in numerical weather Time Correlation of radar prediction can be made ( Brasnett, 1999). Reflectivity with Snow Gauge Snow density is not just needed to Accumulation, Journal of convert QPF’S into snow forecasts. Brian Miretzky 18

Applied Meteorology , 42, (1), 20- D. Lowenthal, S. Cohn, and W. 36. Brown (2004), Mesoscale 5. Roebber, P.J., S.L. Bruening, Snowfall Prediction and D.M. Schultz, and J.V. Cortinas Verification in Mountainous Jr. (2003), Improving Snowfall Terrain, Weather and Forecasting by Diagnosing Snow Forecasting, 19 , (5), 806-828, Density, Weather and doi:10.1175/1520- Forecasting, 18, (2), 264-287, 0434(2004)019<0806. doi:10.1175/1520- Web References 0434(2003)018<0264. Environmental Modeling Center 6. Schmidlin, Thomas W., D.S. http://www.emc.ncep.noaa.gov/mmb Wilks, M. Mckay, R.P. Cember /ylin/pcpverif/daily/ (1995), Automated Quality National Resources Conservation Control Procedure for the “Water Service Equivalent of Snow on the http://www.nrcs.usda.gov/about/orga Ground” Measurement, Journal nization/regions.html of Applied Meteorology, 34, (1), 143-151. A Special Thanks to Dr. Randy 7. Ware, Eric C., D.M. Schultz, Borys for his help with running my H.E. Brooks, P.J. Roebber, S.L. experiment and for supplying the Bruening (2006), NOTES AND mesonet data. Also, for providing a CORRESPONDENCE: hospitable place to stay for the week. Improving Snowfall Forecasting Also, Thanks to Dr. Greg Tripoli for by Accounting for the organizing the trip and providing Climatological Variability of information about the UW-NMS Snow Density, Weather and model its operation and forecasts. Forecasting, 21 , (2), 94-103. Thanks to Andy Thut for use of his 8. Wetzel, M., M. Meyers, R. data and to the rest of the AOS 401 Borys, R. McAnelly, W. Cotton, students for their assistance in A. Rossi, P. Frisbie, D. Nadler, completing this project.

Brian Miretzky 19