National Weather Service-Pennsylvania State University Weather Events

Transcription

1 National Weather Service-Pennsylvania State University Weather Events Southern Express February 2012: Probabilities, Potentials and uncertainty by Richard H. Grumm National Weather Service State College PA Abstract: A strong southern stream short-wave brought snow and severe weather from Texas to Virginia. The severe weather extended from Texas to Georgia as the upper-level wave raced eastward. Farther north and on the cold side of the surface cyclone, snow was observed. The heaviest snow was observed eastern Kentucky to Virginia. Forecasts from most ensemble forecast systems indicated that the high probability outcome would spare the major cities of the northeastern United States from a major snow event. A few NCEP GFS runs did indicate a potential for the storm to track farther north, favoring a precipitation shield as far north as Philadelphia and New York. Some human produced forecasts gravitated toward these low probability GFS forecasts. Incredibly, and despite ensemble forecasts showing a southward tracking storm, just twenty four hours prior to the event a few human forecasters maintained the potential for snow for Washington, Baltimore, and potentially, Philadelphia. This paper will document the event and demonstrate the value of ensembles in forecasting winter storms. It will be shown that the high probability outcomes must be the basis of forecasts to avoid over forecasting the potential of a low probability event. A simple method of assessing predictability is presented to encourage forecasters to make better use of uncertainty in the forecast process.

2 1. INTRODUCTION A strong southern stream short-wave over the Baja on 17 February 2012 raced across the southern United States and out over the western Atlantic Ocean from February 2012 (Fig. 1). A weaker northern stream wave moved through the area about 24 hours before the southern stream wave. The southern stream wave brought severe weather from Texas to Georgia on February (Fig. 2) and snow (Fig. 3) in the cold air north of the surface cyclone track. This was one of the few winter storms to affect the Mid-Atlantic region during the relatively warm and snowless winter of The potential of the storm in a quiet winter likely caused the event to get a lot of attention. It will be shown that most ensemble forecasts suggested this system was a low probability snow event for the major cities of the Mid-Atlantic and northeastern United States. Despite this there were forecasts in the media, on the web, and in social media implying a potential high-impact snow event for much of the East Coast. The ~27km deterministic NCEP GFS had a few runs that showed the potential for a major cyclone along the East Coast (Fig. 4). No GFS forecast issued after 1800 UTC 15 February showed such a strong cyclone forecast as far north and neither did any ensemble forecast system (EFS). It will be shown that the likelihood of a more southern track was in the forecasts and was clearly evident (Junker 2012 Washington Post) in the NCEP EFS. Despite these forecasts, some forecasters and social media sites persisted on predicted a high impact snow as late as 18 February The proper use of ensemble forecast systems (EFSs) can aid in predicting significant weather and aid in predicting the higher probability events. These probabilities aid in dealing with the inherit uncertainty in weather forecasting (Novak et al 2008). A fundamental rule of forecasting is that longer range predictions are subject to more uncertainty than shorter range forecasts. The American Meteorological Society (AMS 2007 states that The ability to resolve the location and timing of weather events decreases as forecast length increases. This paper will show how leveraging predictability issues and probabilistically approaching the forecast situation can reduce over predicting bias. Communicating uncertainty is an important aspect of weather forecasting (Hirschberg et al. 2011) and they stated Forecast uncertainty depends on many factors. Generally, it increases as the forecast lead time (referred to here as forecast lead) increases. Forecast uncertainty also increases more quickly for smaller-scale (size and duration) phenomena, such as tornadoes and thunderstorms, than for larger-scale phenomena, such as a winter storm. Hirschberg (2011) further stated that Despite a growing theoretical understanding of forecast uncertainty and an increasing ability to quantify it with ensemble prediction techniques, deterministic forecasting is still standard for most Hydrometeorological applications. This paper will show that forecasts optimistic for a significant East Coast Storm and the potential for snow in the Mid-Atlantic region as far north as New York, were biased toward deterministic forecasts (see Hirschberg et al 2011) or single forecasts which played into forecast bias. Better forecasts and more likely scenarios could have been readily achieved during this event by properly leveraging EFS data and examining the uncertainty data and by using the ensemble high probability forecasts as the most likely outcomes while being mindful of the low probability forecasts. Forecasts emphasizing a single model do not reflect uncertainty information, gravitating toward a favored solution does not reflect uncertainty information. This is especially problematic at longer forecast ranges. When dealing with significant weather events, such as heavy snow and flooding (Novak 2008) uncertainty guidance needs to be available and conveyed in the forecast process. Consistent information of this nature can only be teased out of an ensemble forecast system. Consistent information about the potential is unlikely to be obtained from analogs or human expectations of model performance.

3 It should be noted that there are still events, even at shorter-ranges which are not predictable. The majority of these events are smaller in scale and shorter in duration. Many of these events are best handled with a mesoscale analysis and monitoring (Bosart 2003). Additionally, uncertainty can affect a few significant synoptic events in the hour range. For example, Zhang et. al (2003) noted this issue with the surprise snowstorm of January During the January event data initial analysis issues and the effects of parameterized convection greatly influenced the forecast outcomes. Bosart (2003) noted that good mesoscale analysis and addressing some basic questions which may have improved shorter-range forecasts of this surprise snow event (see Table 1: Bosart 2003). Thus, EFS should improve our forecasts of high impact events but they are not a panacea as they currently do not cover the full spectrum of predictability. However, only forecasts based on detail analysis, not human emotions, are likely to aid in proving forecasts of these lower predictability events. This paper is intended to show an overview of the storm system and then to demonstrate the power of ensembles to produce relatively reliable forecasts of the high probability outcome and thus avoid over forecasting on the margins of a winter storm. 2. Methods and Data The National Centers for Environmental Predictions (NCEP) Global Forecast System (GFS) is used to re-produce the conditions associated with the event to include the large scale pattern. The standardized anomalies are displayed in standard deviations from normal as in Hart and Grumm (2001) and are computed using the climatology from the NCEP/NCAR global reanalysis data (Kalnay et al. 1996). The focus is on the pattern and anomalies associated with the storm. The pattern and standardized anomalies followed the methods outlined in Hart and Grumm (2001) and the GFS 00-hour forecasts were used to establish the pattern and standardized anomalies. The term R-Climate is used in reference to analysis and forecasts which use reanalysis climate data to diagnose or forecast the departures from normal. The primary ensemble data shown here are from the NCEP Global Ensemble forecast system (GEFS) which as of 1200 UTC 14 February 2012 is run at 55km in horizontal resolution. The Canadian Meteorological Center (CMC) 21-member global ensemble forecast system (CMC- EFS) is also used to show how this event was predicted. The higher resolution NCEP GFS is also shown here and is used to illustrate the problems forecasters can experience relying on a single model or model run. Other EFS and models were available after the fact from the European Center for Medium Range Weather forecasting (ECMWF) TIGGE site. The ECMWF archives models and ensembles from 10 forecast centers, including the Japanese Meteorological Agency (JMA). The JMA model was used by some social media forecasters as it persisted in showing a more northward precipitation shield. For brevity, times are presented as day and hour in the format 15/1800 UTC and 19/0000 UTC which would be 1800 UTC 15 February 2012 and 0000 UTC 19 February 2012 respectively. Fully qualified dates are limited to comparative data from times outside of February The Storm system and impacts i. The large scale pattern The 500 hpa pattern (Fig. 1) showed the strong 500 hpa low over the Baja at 17/0000 UTC. This system weakened as it moved eastward across the southern United States. A weaker northern stream wave moved across the northern United States. The forecasts for a stronger East

4 Coast Cyclone relied on a merger of these two waves. Trough mergers have been known to problematic in predicting cyclogenesis for 20 years (Gaza and Bosart 1990). The strong 250 hpa jet (Fig. 5) shows the strengthening jet and implied jet entrance region developing over the Mid-Atlantic region from 18/1800 UTC though 19/1200 UTC. This was a relatively fast moving system another factor likely to limit higher end snow and precipitation amounts. ii. Regional pattern and key anomalies The GFS 00-hour mean sea-level field (Fig. 6) shows the surface cyclone, with about -1 to -2σ pressure anomalies move though the Gulf States and off the East Coast. The storm, a modest 994 hpa cyclone, was well out to sea off the Coast of Virginia at 20/0600 UTC (Fig. 6f). As with many East Coast Winter Storms (ECWS:Stuart and Grumm 2006) the snow fell north of the cyclone in the region impacted by the strong low-level 850 hpa easterly jet (Fig. 7d-e). The 850 hpa wind anomalies peaked near -4σ above normal north of the cyclone from southern Arkansas to southern Virginia as the storm raced eastward. Though not shown, temperatures on 18 February were relatively warm in the Mid- Atlantic region with highs in southern Virginia ranging from the 50s into the lower 60s. The weak and faster northern stream wave pushed a weak cold front into the region around 19/0000 UTC (Fig. 8a) lowering 850 hpa temperatures to near 0C by 19/0000 UTC. There was evidence of cold air damming by 19/1200 UTC. The precipitation arrived during the day and both 850 hpa and surface temperatures fell rapidly toward the wet-bulb. The new air mass was relatively dry and had a good capacity to cool. At 850 hpa the despite no significant cold air advection at 850 hpa (Fig. 8e-f) the 850 hpa temperatures fell over Virginia and North Carolina. Evaporative cooling was the likely mechanism, as a result of this cooling, some areas had 850 hpa temperatures near - 1σ below normal as the event was winding down at 20/0600 UTC (Fig. 8f). Surface observations between 19/1600 and 19/2300 showed the power of this cooling. The 19/2200 UTC data (Fig. 9) showed that many locations in North Carolina and Virginia had snow with temperatures at or above 0C. At locations where the dew point was 32F or greater had rain. Areas with lower dew points had snow or quickly changed to snow. Warmer temperatures, in the 40s dominated north and east of the precipitation shield from northern Virginia into southern New Jersey. Similarly, temperatures were mainly in the upper 30s northwest of the precipitation shield in Ohio and West Virginia. iii. Forecasts NCEP runs the GFS and the GEFS 4X daily which provides a wide range of forecasts to display. The CMC runs the CMCEFS 2X daily at 0000 and 1200 UTC. It would be prohibitive to show all the available forecasts and cycles. The goal here is to convey the salient points as concisely as practicable. For example, Figure 1 shows 9 GFS surface pressure forecasts. The times shown bracket the 3 forecasts of the strong cyclone produced by forecasts issued at 15/0000, 15/1200 and 15/1800 UTC. Note prior to 15 February there were no strong cyclone forecasts and the weaker more southern track dominated forecasts produced after 16/0000 UTC and of shorter duration (Fig. 4a-e). Figure 10 shows the power of using an ensemble, in this case only using the ensemble mean of the 21 members and climatic anomalies 1. These GEFS forecasts lack the deep cyclone to the north shown in the GFS forecasts. The small standardized anomalies are likely the result of spread between members and the coarser resolution of the GEFS. The GEFS makes use of 1 This is a quick and effective use of ensembles but not the best use of ensembles.

5 perturbed initial conditions to produce different outcomes resulting in the ensemble mean forecast being weaker and farther to the south and east relative to the single GFS. Though not shown, this resulted in a similar south and eastward shift in the precipitation shield. The comparative CMCEFS (Fig. 11) showed a far weaker cyclone with the cyclone sheared out well to the south. The GEFS and CMCEFS forecasts from 15 February strongly showed that the stronger cyclone solution in the deterministic GFS was a low probability forecast. The ensemble mean relative to the GFS data provide little information relative to uncertainty. Experience implies some of uncertainty could be gleaned from the anomalies, which due to a large spread between members and the averaging process, produced less robust mean sea-level pressure anomalies. Figure 12 shows 3 GEFS forecast of mean sea-level pressure with the spaghetti and spread about the mean in the lower panels. These data clearly show the high spread and thus high uncertainty with the pressure field along the East Coast. The two of the more aggressive GEFS runs from 15/0000 and 15/1200 UTC are shown along with the 16/0000 UTC run. The upper panels are from 6-hours prior to those in Fig. 10. These data show a deeper cyclone closer to the coast in the mean in the two forecasts from 15 February relative to the 16/0000 UTC forecasts. However, the lower panels clearly convey the high spread and thus low confidence in the cyclone position and intensity. Though not shown, ensemble sensitivity data indicated most of the issues related to this forecast were due to intensity and position of the cyclone. It is beyond the scope of this paper to examine ensemble sensitivity issues, which in this case provided useful insights into the forecast issues. Suffice to say that the high spread (need spread in hpa currently in pa) was clearly showing large uncertainty. Similar images were produced (not shown) over the periods from February. Select forecasts from 17 and 18 February (Fig. 12) show what one might expect as the forecast length decreased, the spread decreased and thus the confidence increased. Note the 996 hpa contour was all but absent in these forecasts and the spread was ½ that from the earlier forecasts. The shift in the cyclone track and the weaker cyclone in the GEFS produced a comparable shift in the precipitation shield and a southward shift in the cold air. All of which are critical players in predicting snow. The probabilities of QPF to match the cyclone tracks showed a sharp northern edge to the QPF shield. Despite the cyclone tracks the QPF shield had difficulty in the latter runs getting much north of the Pennsylvania border with the high probability outcome confined to Carolinas and southern Virginia. The GEFS, based on the GFS showed a similar shift in the cyclone track (Fig. 10) and the QPF shield (Fig. 14) from forecasts issued on 15 February. Despite this northward shift, the 50% probability of 12.5 mm or more QPF maximized on the 15/1200 UTC GEFS with the northern edge in northern New Jersey. The 16/0000 UTC cycle retreated the threat of 12.5mm of QPF rapidly to the south. Forecasts from 16/1200, 17/0000 and 17/1200 UTC (Fig.15) continued to show a more southward high probability of 12.5mm or more QPF to the south. These forecasts, shorter in forecast length are generally more skillful than longer range forecasts, and they proved to be so. The CMCEFS cyclone track showed a weaker cyclone farther to the south (Fig. 16) and a comparable southward shift in the precipitation shield (Fig. 17). The 21-member CMCEFS was a clear cause for pause. Fortunately, the NCEP GEFS and a super ensemble of these two systems, produced by NCEP and the CMC called the North American Ensemble Forecast System (NAEFS) clearly showed precipitation much north of Washington, DC was a low probability outcome solution as were the forecasts of a deep cyclone off the coast of New York or New Jersey. iv. Observations

6 Stage-IV QPE for the event showed the heaviest precipitation was focused well south of the Mid-Atlantic region (Fig. 18). These data also show that the 12.5mm contour ranged into central Virginia with amounts over 25 mm (1 inch) very limited in extent North Carolina or Virginia. These data support the higher probability QPFs in the ensemble forecasts. The 12-hour increments of the QPE (Fig. 19) show heavier QPF amounts were suppressed well to the south nearer to the Gulf Coast. These data also show how sharp the northern edge of the precipitation shield was. Edges and sharp gradients are ideal locations for uncertainty. The corresponding snowfall based on satellite imagery (Fig. 20) shows how focused the snowfall was over northeastern Tennessee, eastern Kentucky, southern West Virginia, northwestern North Carolina and Virginia. The tight gradient on the northern edge of the storm was the focus for the snowfall. 4. Conclusions A strong southern stream short-wave over the Baja on 17 February 2012 raced across the southern United States and out over the western Atlantic Ocean from February 2012 (Fig. 1). A weaker northern stream wave moved through the area about 24 hours before the southern stream wave. The southern stream wave brought severe weather from Texas to Georgia on February (Fig. 2) and snow (Fig. 3) in the cold air north of the surface cyclone track. This event was an ideal example as to why one should use EFS data in the forecast process. It is also an example of some of the limitations of EFSs which rely on a single model core. And it is an example of why forecasters need to leverage uncertainty data, which requires using ensembles and probabilistic displays to avoid gravitating toward low probability outcomes. The NCEP GEFS showed some of the trends and forecast issues which affected the operational GFS. The forecasts initialized on 15 February, nearly 5-days prior to the onset of precipitation, showed a sharp northward shift in the cyclone (Fig. 10) center and the precipitation shield (Fig. 14). This is clearly a limitation of the GEFS in that it lacks model diversity, relying on perturbations, and thus it tends to follow the same forecast trends displayed by its core model, the GFS. However, the GEFS did show that much north of the Pennsylvania border 12.5 mm or more QPF was a 50-60% outcome, focusing the higher probabilities to the south. It took less than 12 hours for the GEFS QPF shield to shift southward and for the GEFS and the GFS to show a weaker cyclone farther to the south and east. The shorter range GEFS forecasts (Fig. 15& 13 ) showed smaller spread in the cyclone pressure field (higher confidence) and higher probabilities of 12.5 mm of QPF south of Washington, DC. Clearly, shorter range forecasts display greater skill and the spread aids in defining areas of higher confidence. The forecasts from 15 February showed considerably higher spread. Failure to use the EFS data by leveraging the spread and focusing on single solutions diminishes both the role and the value of human forecasters in the forecast process. Bosart (2003) worried about the ability of forecasters to maintain an edge on numerical guidance. Skill in forecasting was clearly shown but much of the skill was directly related to improvement in numerical guidance. Some improvement could be gained by improved diagnosis and analysis. To make a good forecast, 6 basic questions were proposed (Table 1: Bosart 2003). It is proposed in Table. 1 that a set of similar questions could be applied to weather forecasting employing ensemble data to a) ensure that the envelope of solutions is considered b) ensure forecasters are aware of the uncertainty, and c) ensure public forecasts focus on the high probability outcomes. Failure to accomplish this and the continued over reliance on deterministic solutions and ignorance of the uncertainty implies automated processes could produce better unbiased forecasts than humans. Clearly, the deterministic forecast process must whither and die The concept was refined based on personal conversations with Lance Bosart.

7 Table 1. Synoptic Probabilistic Forecasting Process 1) What will most likely happen? 2) Why would this most likely happen? 3) What is the low probability extreme or rare outcome? 4) What is the most uncertain aspect of the forecast? 5) What are the causes of this uncertainty? 6) When will the event most likely occur? This event shows a good example on the value of leveraging uncertainty information in the forecast process. In the majority of strongly forced synoptic events, this approach will likely work well. There probably will be a few events such as the January 2000 even (Zhang et al 2002;Bosart 2003) that may fall through the cracks. They should become rare as forecast system use asynchronous data to update the initial conditions. Smaller scale and locally forced events are likely and will likely remain more difficult to predict and at shorter ranges often require a high state of situational awareness to forecast and warn on. These latter events often have short predictability horizons and can be very sensitive to small problems with initial conditions. A final point here is that no attempt was made herein to explain the source of the uncertainty. Galileo was able to demonstrate how things fell but never explained why. Standard ensemble displays allow us to visualize uncertainty but often do not clarify the why. There are ensemble techniques which allow us to get at the why free of human emotions and bias. 5. Acknowledgements Thanks to the Pennsylvania State University and the National Weather Service in State College for support of to conduct research, ingest these data in real-time, and the ability to conduct case studies. Thanks to Lance Bosart for refining and encouraging the use of the whither and die to deterministic forecast process. Thanks to Lance Bosart (State University of Albany) for conversations on this event and ensemble forecast issues; to Fuqing Zhang (The Pennsylvania State University) for insights related to predictability, and Brian Colle (Stony Brook University) access to and information on ensemble sensitivity issues and the key sensitivity issues associated with this event. Input on the snowfall provided by the National Weather Service Blacksburg, VA. 6. References American Meteorological Society, 2007: An Information Statement of the American Meteorological Society (Adopted by AMS Council on 8 August 2007) Bull. Amer. Meteor. Soc., 88. Bosart, Lance F., 2003: Whither the Weather Analysis and Forecasting Process?. Wea. Forecasting, 18, doi: Gaza, R. S., and L. F. Bosart 1990: Trough merger characteristics over North America. Wea. Forecasting, 5, Graham, Randall A., and Richard H. Grumm, 2010: Utilizing Normalized Anomalies to Assess Synoptic-Scale Weather Events in the Western United States. Wea. Forecasting, 25, Grumm, R.H 2011: The Central European and Russian Heat Event of July-August 2010.BAMS, 92, Grumm, R.H. and R. Hart. 2001: Standardized Anomalies Applied to Significant Cold Season Weather Events: Preliminary Findings. Wea. and Fore., 16,

8 Hart, R. E., and R. H. Grumm, 2001: Using normalized climatological anomalies to rank synoptic scale events objectively. Mon. Wea. Rev., 129, Hirschberg, Paul A., and Coauthors, 2011: A Weather and Climate Enterprise Strategic Implementation Plan for Generating and Communicating Forecast Uncertainty Information. Bull. Amer. Meteor. Soc., 92, doi: Novak, D. R, D.R. Bright, M.J. Brennan, 2008: Operational Forecaster Uncertainty needs and future roles. WWAF,23, Washington Post, 2012: Inside nearly impossible winter weather forecast. 17 February Zhang, F., C. Snyder, and R. Rotunno, 2002: Mesoscale predictability of the 'surprise' snowstorm of January Monthly Weather Review, 130,

9 Figure 1. NCEP GFS 00-hour forecasts of 500 hpa heights (m) and height anomalies in 24 hour increments from a) 0000 UTC 17 February 2012 through f) 0000 UTC 22 February Heights every 60m and anomalies in standard deviations based on the color bar. The green dot is the approximate location of Blacksburgh, Virginia Return to text.

10 Figure 2. Storm reports from the Storm prediction center (SPC) showing severe weather by type as in the color key. Upper image is for 17 February and lower image is for 18 February Return to text.

11 Figure 3. Snow fall totals (in) from NWS public information statements ending on 20 February Return to text.

12 Figure 4. NCEP GFS forecasts of mean sea-level pressure (hpa) and mean sea-level pressure standardized anomalies valid at 0000 UTC 20 February 2012 from successive 6-hour GFS forecasts from a) 0000 UTC 17 February through i) 0000 UTC 15 February Isobars are every 4 hpa. Return to text.

13 Figure 5. As in Figure 1 except for 250 hpa winds (kts) and wind anomalies in 6-hour increments from a) 1800 UTC 18 February 2012 through f 0000 UTC 20 February Return to text.

14 Figure 6. As in Figure 5 except for GFS mean sea-level pressure (hpa) and pressure anomalies from a) 0000 UTC 19 February through f) 0600 U 20 February Return to text.

15 Figure 7. As in Figure 6 except for 850 hpa winds and wind anomalies. Return to text.

16 Figure 8. As in Figure 7 except for 850 hpa temperatures (C) and temperature anomalies. Return to text.

17 Figure 9. Surface observations focused over Virginia at 2200 UTC 19 February Temperatures and weather are cooler coded in distinct bands. Data courtesy of Robert Hart and CoolWx.com. Return to text.

18 Figure 10. As in Figure 1 except for NCEP 21-member 55km GEFS ensemble mean and anomalies valid at 0000 UTC 20 February 2012 from forecasts initialized at a) 0000 UTC 17 February, b) 1800 UTC 16 February, c) 1200 UTC 16 February, d) 0600 UTC 16 February, e) 0000 UTC 16 February, f) 1800 UTC 15 February, g) 1200 UTC 15 February, h) 1200 UTC 15 February, and i) 1200 UTC 14 February Return to text.

19 Figure 11. As in Figure 10 except for the 21-member CMCEFS initialized at a) 1200 UTC 17 February, b) 0000 UTC 17 February, c) 1200 UTC 16 February, d) 0000 UTC 16 February, e) 1200 UTC 15 February, f) 0000 UTC 15 February. Return to text.

20 Figure 12. NCEP GEFS forecast valid at 1800 UTC showing the ensemble mean and anomalies in the upper panels and each members 996 and 1020 hpa contour with the spread about the mean (hpa) in the lower panels for GEFS initialized at a&d) 0000 UTC 15 February, b&f) 1200 UTC 15 February, c&g) 0000 UTC 16 February Return to text.

21 Figure 13. As in Figure 12 except GEFS initialized at a&d) 1200 UTC 16 February, b&f) 0000 UTC 17 February, c&g) 1200 UTC 17 February Return to text.

22 Figure 14. NCEP GEFS forecast of 12.5 mm or more QPF. Upper panels show the probability of 12.5mm of QPF and the ensemble mean 12.5 QPF contour, lower panels show the mean QPF (shaded) and each members 12.5 mm contour from GEFS initialized at a-d) 0000 UTC 15 February 2012, c-e) 1200 UTC 15 February 2012, and c-f) 0000 UTC 16 February Return to text.

23 Figure 15. As in Figure 14 except for GEFS initialized at a-d) 1200 UTC 15 February 2012, c-e) 0000 UTC 17 February 2012, and c-f)1200 UTC 17 February Return to text.

24 Figure 16. As in Figure 12 except of CMC 21-member ensembles initialized at a-e) 0000 UTC 15 February 2012, b-f) 1200 UTC 15 February 2012, and c-g) 0000 UTC 16 February Return to text.

25 Figure 17. Figure 16. As in Figure 14 except of CMC 21-member ensembles initialized at a-e) 0000 UTC 15 February 2012, b-f) 1200 UTC 15 February 2012, and c-g) 0000 UTC 16 February Return to text.

26 Figure 18. Stage-VI estimated liquid equivalent precipitation (mm) for the 48 hour period ending at 12Z20Feb2012. Return to text.

27 Figure 19. As in Figure 18 except 12-hour accumulations of QPE for the periods ending a) 0000 UTC 19 February, b) 1200 UTC 19 February, c) 0000 UTC 20 February and d) 1200 UTC 20 February Return to text.

28 Figure 20. Satellite image showing the snowfall area from a visual perspective on 20 February This suggests missing snowfall data in the map produced for Figure 3. Image provided by Steve Keighton, NWS Blacksburg, VA. Return to text.