Classification and Conceptual Models for Heavy Snowfall Events over East Vancouver Island of British Columbia, Canada

Transcription

1 OCTOBER 2013 W U E T A L Classification and Conceptual Models for Heavy Snowfall Events over East Vancouver Island of British Columbia, Canada MINGLING R. WU Pacific Storm Prediction Centre, and National Laboratory for Coastal and Mountain Meteorology, Environment Canada, Vancouver, British Columbia, Canada BRADLEY J. SNYDER Pacific Storm Prediction Centre, Environment Canada, Vancouver, British Columbia, Canada RUPING MO National Laboratory for Coastal and Mountain Meteorology, Environment Canada, Vancouver, British Columbia, Canada ALEX J. CANNON Pacific Climate Impacts Consortium, University of Victoria, Victoria, British Columbia, Canada PAUL I. JOE National Laboratory for Coastal and Mountain Meteorology, Environment Canada, Vancouver, British Columbia, Canada (Manuscript received 24 September 2012, in final form 13 May 2013) ABSTRACT The East Vancouver Island region on the west coast of Canada is prone to heavy snow in winter due to its unique geographical setting, which involves complicated interactions among the atmosphere, ocean, and local topography. The challenge for operational meteorologists is to distinguish a weather system that produces extreme snow amounts from one that produces modest amounts in this region. In this study, subjective, objective, and hybrid classification techniques are used to analyze the characteristics of 81 snowstorms observed in this region over a 10-yr period ( ). It is demonstrated that there are four principal weather patterns (occluded front, lee low, warm advection, and convective storm) conducive to heavy snow in East Vancouver Island. The occluded front pattern is the most ubiquitous for producing snow events, while the lee low pattern is the most extreme snow producer that poses the biggest forecast challenge. Based on the identified weather patterns and a further investigation of five key weather ingredients, four conceptual models are developed to illustrate the meteorological processes leading to significant snowfalls in East Vancouver Island. These conceptual models have the potential to help meteorologists better understand and identify weather systems that would produce heavy snowfalls in this region and, therefore, improve forecasting and warning performance. 1. Introduction East Vancouver Island (EVI) is a coastal region in the Georgia Basin of British Columbia (BC), Canada (Fig. 1). This region is characterized by topographic extremes, with the Vancouver Island Ranges to the southwest and Corresponding author address: M. R. Wu, Pacific Storm Prediction Centre, Environment Canada, Burrard St., Vancouver BC V6C 3S5, Canada. rodger.wu@ec.gc.ca the Georgia Strait to the northeast. Its winter climate is dominated by a westerly Pacific airstream descending along the east-facing mountain slopes, which tend to disperse cloud and lessen rainfall events (Boughner 1937; Lange 1998). However, locally heavy snowfalls can be expected from time to time, especially when the Georgia Strait is dominated by cold northeast outflows from the mainland inlets during arctic outbreak events (Jackson and Steyn 1994; Stewart et al. 1995; Taylor 1996). The complicated interactions among the atmosphere, ocean, and local topography make these snowstorms DOI: /WAF-D Ó 2013 American Meteorological Society

2 1220 W E A T H E R A N D F O R E C A S T I N G VOLUME 28 FIG. 1. Topography and geography in the vicinity of Vancouver Island. The locations of the following weather stations are indicated by their identifiers (blue): West Sea Otter buoy (46204), La Perouse Bank buoy (46206), Port Hardy (CYZT), Campbell River (CYBL), Comox (CYQQ), Nanaimo (CYCD), Quillayute (KUIL), Aldergrove Radar (CWUJ), Pam Rocks (CWAS), and Squamish (CWSK). EVI is divided into three subregions: northern (N), central (C), and southern (S). A larger domain is shown as an embedded map in the bottom-left corner. the most significant forecast challenge to operational meteorologists. Low-resolution numerical weather prediction (NWP) models usually underforecast snowfall amounts in this region. In the past few years, ensemble prediction systems and high-resolution NWP models have been available for operational forecasts (Buizza et al. 2005; Erfani et al. 2005). Automated processing of Doppler radar data has also been developed for severe weather warnings (Joe et al. 2012). These new technologies certainly have the potential to improve the forecast skill of high-impact weather in complex terrain. However, a preliminary verification showed that heavy snow events in EVI during the 10-yr period from 2000 to 2009 were still underforecast by 25 cm on average (Wu 2011). For a region where the snowfall warning criterion was only 5 cm day 21, this level of underprediction is significant. It has been recognized that outbreaks of arctic air are an effective source of heavy snow over EVI: northeasterly winds through the coastal valleys and fjords from the BC interior absorb moisture from the Georgia Strait to generate localized heavy snow along the east coast of Vancouver Island (Jackson and Steyn 1994; Stewart et al. 1995; Taylor 1996; Geng et al. 2012). A variant of this scenario occurs when a Pacific low pressure system approaches Vancouver Island (Joseph 1992; Stewart et al. 1995; Mass 2008; Geng et al. 2012). Such a system usually maintains a warm conveyor belt on its southeastern section (Carlson 1980), which transports moist marine air to the BC south coast. Meanwhile, the cyclone draws cold air out of the mainland inlets into the Georgia Strait. The joint action of flow convergence and orographic lift results in rapid air ascent and heavy snow over EVI. Occasionally, the warm conveyor belt is accompanied by a narrow atmospheric river with large moisture content (Zhu and Newell 1994), which can produce extreme precipitation, including heavy snow, along coastal BC (Lackmann and Gyakum 1999; Geng et al. 2012). Another kind of heavy snow over EVI is generated by postfrontal convection, as noted by Ranahan (1978). These convective snowstorms can be triggered by maxima of positive vorticity advection (PVA) in the midtroposphere (Maddox and Doswell 1982; Johnson and Mortimer 1984; Zwatz- Meise and Bendl 1992; Bader et al. 1995). In this study, various snowstorm scenarios in EVI are investigated. The main goals are to 1) identify principal weather patterns that lead to heavy snow in EVI and 2) search for key weather ingredients that contribute to heavy snow in each pattern. The findings are used to develop conceptual models to facilitate operational forecasts. Descriptions of data and classification techniques, together with a brief verification of snowfall warnings for EVI, are given in section 2. The principal weather patterns leading to heavy snow in EVI are described in section 3. Key weather ingredients associated with the principal weather patterns are examined in section 4. Conceptual models illustrating the interactions between synoptic-scale flows and mesoscale terrain features are presented in section 5. A preliminary evaluation of the conceptual models is presented in section 6. Concluding remarks are given in section Data, warning performance, and methodology a. Data and NWP model descriptions A 12-yr ( ) dataset of operational weather prediction products is available from the Pacific Storm Prediction Centre (PSPC) of Environment Canada. This dataset contains various weather observations (hourly weather reports, radar and satellite images, etc.), NWP model guidance, and detailed warning information for severe weather events. This dataset has only been consistently archived at PSPC since Therefore, this study is limited to a relatively short period. Within our study period, routine NWP forecast guidance was provided mainly by the Global Environmental Multiscale (GEM) regional forecast system (hereafter GEM-R) of the Canadian Meteorological Centre (CMC), which was implemented in 2004 with horizontal resolution increasing from 24 to 15 km (Mailhot et al. 2006). Since 2007, a limited-area model of the GEM system with a higher resolution of 2.5 km (hereafter LAM2.5) has been run at CMC (Erfani et al. 2005). Forecast

3 OCTOBER 2013 W U E T A L products from both GEM-R and LAM2.5 are used to evaluate the NWP guidance in this study. Synoptic weather features associated with the EVI snowstorms are analyzed using the North American Regional Reanalysis (NARR) dataset, which has a 32-km resolution at 3-h intervals (Mesinger et al. 2006) and is available from the National Oceanic and Atmospheric Administration s (NOAA) National Operational Model Archive and Distribution System ( noaa.gov/). Both the composite and classification analyses in the following sections are based on this dataset. Fields analyzed include 500- and 700-hPa geopotential height, hPa geopotential height thickness, precipitable water, 850-hPa temperature and winds, 2-m temperature, and mean sea level pressure. In addition, Special Sensor Microwave Imager (SSM/I; Wentz 1997) data, available from Remote Sensing Systems ( are used to analyze moisture supply for the snowstorms in EVI. b. Snowfall warning verification The snowfall warning criterion for EVI in our study period was 5-cm accumulation within 24 h. Based on this criterion, 88 snowfall warnings were issued, and 81 events with snow accumulation reaching 5 cm or more were observed in EVI during the 10-yr period from 2000 to In this study, conceptual models for snowfall events in EVI are based on the detailed analysis of these 81 events. Snowfall events observed during the 2010/11 winter season are used to validate the performance of these conceptual models. Following the PSPC convention, observed snow events in EVI are categorized into three groups: regular event (5 10 cm day 21 ), major event (11 30 cm day 21 ), and extreme event (greater than 30 cm day 21 ). The term heavy snow will be used to cover the major and extreme snowfall events. A verification of snowfall warnings issued for EVI during the period is shown in Fig. 2. Among the 88 warnings, 49 (55.7%) were hits H, 27 (30.7%) were misses M, 6 (6.8%) were false alarms F, and 6 (6.8%) were unverifiable (Fig. 2a). A popular score used to measure the accuracy of binary weather forecasts or warnings is the critical success index (CSI; see Wilks 2006), which is computed as CSI 5 H/(H 1 M 1 F). (1) The CSI for snowfall warnings issued during this period is 0.59, which is close to the average accuracy of warning performance at PSPC (Tatar 2006). For the 49 hit events, the observed snow amount of each event is compared to the forecast amount. As shown in Fig. 2b, the snow amounts given in the warning FIG. 2. (a) The percentages of hits, misses, false alarms, and unverifiable events among the 88 snowfall warnings issued by the Pacific Storm Prediction Centre for the period. (b) Average underforecast snow amounts given in the warning bulletins (gray bars) and in the NWP model guidance from the Canadian GEM-R model (black bars) for the total events, major events, and extreme events. bulletins were underforecast by 8 cm on average and by 26 cm for extreme events. For comparison, routine NWP model guidance underforecast by 12 cm on average and by 34 cm for extreme events. c. Snowfall event classification Pattern recognition is a basic instinct of operational meteorologists. Through subjective assessments of realtime weather information, experienced forecasters can identify crucial signals of upcoming storms. This approach is used here to identify important synoptic- and mesoscale weather features relevant to the snowstorms in EVI. The identified features are then subjectively classified into several groups. The procedure begins by determining the period of each snowstorm during which at least moderate snow (i.e., visibility restriction #1 km or radar-detected snow rate $ 0:5cmh 21 ) was continuously observed and the total snowfall accumulation reached 5 cm or higher. Next, the main features conducive to snowfall are identified within the snow period through careful examination of surface weather charts, atmospheric soundings, and satellite and radar imagery.

4 1222 W E A T H E R A N D F O R E C A S T I N G VOLUME 28 Finally, events with similar features are grouped into several classes. The degree of similarity and difference between individual events is subjectively determined based on our operational forecast experience. It is understood that this manual classification is inherently subjective and may not be reproducible by different analysts. Reproducibility is especially important when a classification system is to be applied operationally to new events. To address subjectivity in assigning the historical events to classes, the classification procedure was performed on the same dataset, quasi-independently, by two authors of this study; see section 3a for further details. There are also many objective methods for pattern recognition and classification (e.g., Yarnal 1993; Cannon et al. 2002; Cannon 2012). In this study, the K-means clustering method (see Wilks 2006) is used to complement the manual classification of snowstorms in EVI. In our application, the Euclidean distance of the hPa thickness from NARR is used to measure the degree of similarity between individual events and group centroids. Note that the hPa thickness is proportional to the mean virtual temperature in the lower troposphere, which is a crucial regulator of snow observed on the ground. This variable also contains combined information of midlevel and near-surface flows. A potential disadvantage of the K-means algorithm is the need to prespecify the number of groups and their initial membership, which can lead to subjectivity in the resulting classification (Wilks 2006). Additionally, K-means clustering, when applied to synoptic-scale circulation variables without explicitly taking into account the local phenomenon of interest, can yield classes that have little local meteorological significance or distinctiveness (Frakes and Yarnal 1997; Cannon 2012). To avoid these weaknesses, we also use a hybrid scheme to blend the K-means method with the manual classification. As proposed by Frakes and Yarnal (1997), the hybrid scheme uses the subjectively determined synoptic classes as seeds for the objective classification; see section 3d for further details. If the resulting classes are similar to those derived by the manual classification, new events can then be assigned to the appropriate classes automatically and reproducibly using NARR or NWP outputs. 3. The principal weather patterns a. Manual classification To provide a better understanding of the snowstorms in EVI, we first used the manual classification technique outlined in the previous section to analyze the 81 snowfall events in EVI over the period. Based on his forecast experience and communications with other PSPC operational forecasters, the first author of TABLE 1. Number (percentage) of regular (5 10 cm day 21 ), major (11 30 cm day 21 ), and extreme (greater than 30 cm day 21 )snowfall events, out of the 81 events observed in EVI during the 10-yr period from 2000 to 2009, assigned to the five groups from the manual classification. The five groups consist of four principal weather patterns (OF, LL, WA, CS) and a group of other events (OT). Group Regular Major Extreme Total OF 9 (23%) 9 (33%) 5 (33%) 23 (28%) LL 6 (15%) 6 (22%) 7 (47%) 19 (24%) WA 10 (26%) 6 (22%) 2 (13%) 18 (22%) CS 9 (23%) 5 (19%) 1 (7%) 15 (19%) OT 5 (13%) 1 (4%) 0 (0%) 6 (7%) Total 39 (100%) 27 (100%) 15 (100%) 81 (100%) this study (MRW) examined the main features of these snowfall events and identified four principal weather patterns that can be used to characterize most of these events: occluded front (OF, 26 events), lee low (LL, 17 events), warm advection (WA, 14 events), and convective storm (CS, 14 events). Upon agreeing to the main features of these principal patterns, an independent reanalysis was performed by a coauthor (RM) on the same dataset using the same subjective technique. A cross validation of these two analyses determined the final assignment of pattern membership for the 81 events. As summarized in Table 1, there were 23 OF events (28%), 19 LL events (24%), 18 WA events (22%), and 15 CS events (19%). The other six events (7%) are dissimilar and cannot be grouped into any of these four principal patterns. They will be referred as other (OT) patterns. Before describing the main characteristics of the principal weather patterns, four case studies are presented in the next subsection to illustrate the snowstorms and highlight their forecast challenges. For reference, Fig. 3 shows satellite images of four weather systems that would later produce heavy snow in EVI. b. Four case studies 1) WA EVENT In Fig. 3a, a typical warm conveyor belt associated with a Pacific frontal system had moved onshore over the BC south coast at 0600 UTC 16 December Light snow began to fall in EVI ahead of the surface warm front around 0400 UTC. The weather reports from the airport at Canadian Forces Base Comox (CYQQ) indicated light snow changing to moderate snow at 0522 UTC and to heavy snow at 0654 UTC. Overall, the storm produced an unexpected maximum of 50 cm of snowfall in the Comox area and 46 cm at Campbell River (CYBL). A snowfall warning was issued by PSPC around 1800 UTC 15 December. The warning is considered a hit. However, based on the NWP guidance of GEM-R (24-km resolution), only 5 cm of snow was mentioned in the initial

5 OCTOBER 2013 W U E T A L FIG. 3. Satellite images with weather features of four different systems that brought heavy snowfall to EVI. (a) WA associated with a warm conveyor belt landing over the BC south coast. (b) An OF approaching Vancouver Island. (c) A CS off Vancouver Island. (d) An LL off Vancouver Island. The location of Vancouver Island was marked by a yellow cross. Low and high centers of mean sea level pressure are marked by L and H, respectively. Green arrows represent low-level flows. Orange double-dashed lines are 500-hPa troughs. The orange star in (b) indicates the 500-hPa low center. Fronts are based on the PSPC weather analysis charts. warning bulletin. It was expected that the warm-air advection would cause snow changing to rain over the BC south coast. For EVI, however, the warm, moist southwest flow from the Pacific was blocked and elevated by the Vancouver Island Ranges, and the northeast outflow from the mainland inlets maintained a cold boundary layer in the area until 1500 UTC. 2) OF EVENT Figure 3b shows an occluded marine cyclone at 1800 UTC 14 December 2009 with warm-air advection heading toward the U.S. Pacific Northwest and an OF heading toward coastal BC, where a quasi-stationary arctic front preexisted. Note that a 500-hPa low, marked by an orange star, was almost collocated with the surface low over the North Pacific. Light snow began to fall in EVI around this hour and changed to moderate snow in the evening (0300 UTC). A total snowfall amount of 20 cm was recorded at Campbell River. This storm was well forecasted: a snowfall warning for EVI was issued at 2330 UTC 13 December 2009, with forecast amounts up to 15 cm and later upgraded to 20 cm. The NWP model guidance indicated up to 10 cm from GEM-R (15-km resolution) and up to 15 cm from LAM2.5 in EVI. Associated with the OF in Fig. 3b was a cyclonically ascending airstream that originated in the warm-sector

6 1224 WEATHER AND FORECASTING VOLUME 28 FIG. 4. Model predictions and radar observation of a convective storm over southern Vancouver Island on 10 Jan 2007 (also see the satellite imagery in Fig. 3c). (a) Precipitation rate (mm h21) from the 30-h forecast of GEM-R, valid at 0600 UTC. (b) As in (a), but from the 18-h forecast of LAM2.5. (c) Reflectivity (dbz) and snow rate (cm h21) from the Aldergrove Radar (CWUJ), valid at 1000 UTC. (d) As in (b), but valid at 1000 UTC. Note that the echo circled by a white oval in (c) indicates snow over the central section of EVI, which was underforecasted by the LAM2.5 model. boundary and flowed through the occluded portion of the system. This feature is sometimes referred to as a trough of warm air aloft (Penner 1955; Martin 1999). 3) CS EVENT Figure 3c shows a band of convective cells off Vancouver Island at 0600 UTC 10 January This intense CS was associated with a maximum of PVA ahead of a 500-hPa short-wave trough in the wake of a surface cold front. Figure 4 shows that this mesoscale feature was well predicted by the NWP models (both GEM-R and LAM2.5). As compared with the radar observation at 1000 UTC (Fig. 4c), the high-resolution model (GEM2.5; Fig. 4d) correctly predicted the snowband over the southern end of Vancouver Island, but underforecasted the snowband in the central section of EVI (i.e., the radar echo circled in Fig. 4c).

7 OCTOBER 2013 W U E T A L Several locations in the central and southern sections of EVI recorded cm of snow from this convective snowstorm. The GEM-R model indicated 5 10 cm of snow for EVI and the LAM2.5 model suggested cm over southern EVI. However, no snowfall warning was issued because forecasters were not confident with the model predictions. Instead, a wind warning was in effect as this convective storm moved across EVI. 4) LL EVENT Figure 3d shows a cyclonic circulation pattern off Vancouver Island at 1800 UTC 25 February The cyclone can be considered as a lee low induced by the northeasterly outflow across the Vancouver Island Ranges. The cyclonic circulation spread southeast winds to the southern section of the Georgia Strait. A cross-strait low-level convergence zone with enhanced precipitation was created when the moist southeast flow met with the dry northeast outflow or was blocked by the local topography, as shown in Fig. 5. Snow over southern EVI began at 1700 UTC 25 February and ended at 1400 UTC of the following day. Nanaimo Airport (CYCD) reported a total snowfall amount of 25 cm. A nearby station recorded a total amount of 40 cm. A snowfall warning was issued at 2330 UTC 24 February, predicting 5 10 cm of snow in EVI. This warning was updated at 0545 UTC 25 February, predicting local amounts up to 15 cm over southern EVI. The GEM-R model predicted rain changing to snow in the evening, with a snowfall amount of 5 cm in EVI. The LAM2.5 model suggested 5 10 cm of snow in the southern section of EVI. Note that the LAM2.5 model generally produced more and better snowfall amounts than the GEM-R model did. This is consistent with the fact that the high-resolution model (LAM2.5) is equipped with better terrain and a microphysical condensation scheme for explicit predictions of precipitation amounts and types (Erfani et al. 2005). The GEM-R precipitation types were derived from a diagnostic routine mainly dependent on the model forecast temperature profiles (Bourgouin 2000). c. Composite analysis Some common features of categorized weather patterns can be highlighted through the analysis of their composite structure. In this subsection, composite analysis is performed for each of the four principal snowfall categories based on the NARR data. To generate the composites, the beginning hour of each event is shifted to the nearest hour at which the NARR data are available. The composite fields are the ensemble averages of each principal category with respect to this adjusted beginning hour of the snowstorm. 1) THE WA PATTERN Figure 6 shows composite charts based on the 18 WA events. At the 500-hPa level (Fig. 6a), there is a noticeable trough off the west coast of Alaska and a much weaker trough over the Gulf of Alaska. A weak ridge can be seen over BC along the Coast Mountain Ranges. This short-wave pattern is more evident at the 700-hPa level (Fig. 6b). The warm-air advection is clearly indicated by a tongue of moist air in Fig. 6b and an 850-hPa southwesterly jet of kt (1 kt m s 21 )infig.6c. The surface circulation (Fig. 6d) is characterized by a low pressure center in the Gulf of Alaska and a subtropical high off the west coasts of the United States and Mexico. This pressure pattern supports strong southeasterly winds through the Georgia Strait and moderate northeasterly outflow from the BC mainland inlets toward Vancouver Island. The convergence of these two flows can give rise to heavy snow in EVI. 2) THE OF PATTERN Figure 7 shows composite charts of the 23 OF events. Both the 500- and 700-hPa levels are characterized by a moderate ridge over the Bering Sea (1708W) and a deep trough over the Gulf of Alaska. Under the 500-hPa trough is an intense surface cyclone with center sea level pressure below 1000 hpa off the BC central coast (Fig. 7d). This is a typical setup for a cyclone in its mature stage. In Fig. 7b, a tongue of moist air off Vancouver Island suggests the location of the occluded front. The 850-hPa wind field (Fig. 7c) illustrates a low-level jet with southwest winds of kt toward the Pacific Northwest and Vancouver Island. 3) THE CS PATTERN Figure 8 shows composite charts of the 15 CS events. This pattern usually occurs in the wake of a passing cold front. The 500-hPa pattern (Fig. 8a) bears some resemblance to its OF counterpart (Fig. 7a), except for a stronger ridge over the Bering Sea and a deeper trough located farther to the east. A stronger ridge guides the cold arctic air to the northeast Pacific, leading to unstable conditions (cold air aloft) off the BC and Washington coasts. The precipitable water (PWAT) field in Fig. 8b suggests that the air mass near Vancouver Island in this CS pattern is drier than its WA (Fig. 6b) and OF (Fig. 7b) counterparts. The 850-hPa wind field (Fig. 8c) shows that the northwesterly flow over the northeast Pacific can be traced to its origin at higher latitudes. The surface circulation (Fig. 8d) is characterized by an inverted trough along the BC and Alaska coasts and a strong subtropical high extending northwestward to the central North Pacific.

8 1226 WEATHER AND FORECASTING VOLUME 28 FIG. 5. The observed and predicted GSCZ, valid at 0600 UTC 26 Feb (a) Reflectivity (dbz) and derived snow rate (cm h21) from CWUJ. (b) As in (a), but for the height of the echo top. (c) The water equivalent of the snow rate (mm h21) from the 18-h prediction of the LAM2.5 model. (d) As in (c), but for the predicted winds. 4) THE LL PATTERN Figure 9 shows composite charts of the 19 SL events. A strong ridge over the North Pacific extending across Alaska into the Arctic Ocean and a deep trough across western Canada are the dominant features at the 500and 700-hPa levels. Northerly flow ahead of a ridge ushers cold arctic air into the BC interior and strong arctic outflow to coastal BC. A closed cyclonic circulation just over Vancouver Island is also shown in the 850-hPa wind field (Fig. 9c) and the sea level pressure field (Fig. 9d). This can be considered to be a lee low induced by the strong arctic outflow across the Coast Mountains and the Vancouver Island Ranges. These composite features of the LL pattern are similar to those associated with snowstorms in Puget Sound (Ferber et al. 1993). Figure 10 plots both the composites (i.e., ensemble means) and the standard deviations of 500-hPa geopotential heights of the four principal patterns. It is shown that the composites for every pattern are quite reliable in the vicinity

9 OCTOBER 2013 WU ET AL FIG. 6. Composite weather charts of 18 snowfall events in EVI associated with the WA pattern: (a) hPa thickness (dam, color filled) and 500-hPa geopotential height (dam, solid lines at 6-dam intervals), (b) PWAT (kg m22, color filled) and 700-hPa geopotential height (dam, solid lines at 6-dam intervals), (c) 850-hPa temperature (8C, color filled) and wind (one full wind barb represents 10 kt), and (d) temperature at 2 m above ground (8C, color filled) and mean sea level pressure (hpa, solid lines at 4-hPa interval). of EVI, where the standard deviations are relatively small. Larger variance can be found in the upper stream areas, especially for the LL pattern and the WA pattern. d. Objective and hybrid classification Our manual classification process is based on the operational meteorologists experience and intuitive understanding of regional weather systems related to the snowstorms in EVI. However, the approach is inherently subjective and may be difficult to reproduce by other forecasters in an operational context. As a complement to the subjective classification, an automated K-means cluster analysis is applied to historical events from 2000 to The K-means algorithm can be found in Wilks (2006). In our application, the K-means clustering analysis is based on NARR hPa thickness at the beginning of the snowstorms in EVI. More specifically, the beginning hour of each event was shifted to the nearest hour at which the 3-hourly NARR data were available. Thickness data at the adjusted beginning hour over the same

10 1228 WEATHER AND FORECASTING VOLUME 28 FIG. 7. As in Fig. 6, but for 23 snowfall events in EVI associated with the OF pattern. domain as in Fig. 6a, as well as the same data 3 h earlier and 3 h later, were used to feed the K-means algorithm. To mimic the four principal patterns identified in our manual classification, the number of groups is set to four. The K-means algorithm was run twice in this study. In the first run, the 81 snowfall events were randomly initialized into four groups. The final four groups from this objective classification are denoted as OG1, OG2, OG3, and OG4. The second run took the four principal classes from the manual classification as the four initial groups, and then randomly assigned the six outliers to these four groups before executing the K-means iteration. It thus resulted in a hybrid classification system guided by forecasters knowledge. The final four groups of this hybrid approach are denoted as HG1, HG2, HG3, and HG4. Table 2 gives a summary of the group memberships of the 81 snowfall events in EVI determined from the objective and hybrid K-means runs, with respect to the five classes from the manual classification. There were 24 events being grouped into OG1, among which 15 were LL events, 5 OF events, 2 WA events, 1 CS events, and 1 outlier from the manual scheme. Apparently, this group is closest to the LL category. For the 22 events in

11 OCTOBER 2013 WU ET AL FIG. 8. As in Fig. 6, but for 15 snowfall events associated with the CS pattern. OG2, there were 11 OF events, 7 WA events, 2 CS events, and 2 outliers. Therefore, this group is closest to the OF category. It appears that the objective K-means procedure fails to separate the WA and CS events into two distinct groups. In other words, most of the WA and CS events are mixed up in OG3 and OG4. The classifications from the hybrid procedure are more consistent with the manual classifications. As shown in Table 2, 16 of the 29 HG1 events are in the LL category, 12 of the 19 HG2 events are in the OF category, 10 of the 19 HG3 events are in the CS category, and 10 of the 14 HG4 events are in the WA category. Figure 11 shows the composite and standard deviation charts of 500-hPa geopotential heights for the four groups from the hybrid approach. The pattern resemblances between HG1 and LL, HG2 and OF, HG3 and CS, HG4 and WA are evident. Generally speaking, the standard deviations in Fig. 11 are smaller than their counterparts in Fig. 10, suggesting more reliable and consistent results from the hybrid approach. 4. Key weather ingredients Wetzel and Martin (2001) developed an ingredientsbased methodology for operational analysis and

12 1230 WEATHER AND FORECASTING VOLUME 28 FIG. 9. As in Fig. 6, but for 19 snowfall events in EVI associated with the LL pattern. prediction. Their study identified five important ingredients for midlatitude winter precipitation: forcing for ascent, moisture, instability, precipitation efficiency, and temperature. Following the same methodology, Wu (2011) conducted careful examinations of the 81 snowstorms in EVI and identified 10 key weather ingredients for these storms. The following five ingredients are considered to be the most important elements in the storm development: 1) moisture supply, 2) arctic outflow, 3) Georgia Strait convergence zone (GSCZ), 4) instability, and 5) surface low track. As compared to the five ingredients found in Wetzel and Martin (2001), the arctic outflow makes a considerable contribution to the forcing for ascent and a significant contribution to the cold temperature for snowstorms in EVI. The GSCZ makes a major contribution to precipitation efficiency. Operational meteorologists should pay close attention to these elements in their routine assessments of snowstorm potential in EVI. a. Moisture supply Sufficient moisture supply must be available for any storm to produce heavy precipitation. For storms in EVI, the moisture origin may also have a significant impact on

13 OCTOBER 2013 WU ET AL FIG. 10. The composites (dam, solid lines at 6-dam intervals) and standard deviations (dam, color filled) of 500-hPa geopotential heights of the four principal patterns from the manual classification procedure. (a) The LL pattern with 19 events. (b) The OF pattern with 23 events. (c) The CS pattern with 15 events. (d) The WA pattern with 18 events. the precipitation type. Operational meteorologists usually rely on the satellite imagery derived from water vapor (WV) emissions at wavelengths from 6 to 7 mm to identify moisture in the weather system. Because these wavelengths are not within an atmospheric window, emissions from low-level WV are not normally detectable (Bader et al. 1995). To overcome this problem, we also used the SSM/I data to analyze the moisture supply. These data from well-calibrated satellite microwave radiometers have the capability to retrieve profiles of atmospheric water over the ocean (Wentz 1997). Our analysis of the SSM/I and WV data indicates that the predominant moisture sources are the BC north and central coasts for the LL events, the Gulf of Alaska for the CS events, and from the subtropics to midlatitudes of the North Pacific for the WA and OF events. Among the

14 1232 W E A T H E R A N D F O R E C A S T I N G VOLUME 28 TABLE 2. Group memberships and cross tabulation of the 81 snowfall events in EVI based on the manual, objective, and hybrid classification schemes. The manual procedure assigned 75 events into four principal categories (OF, LL, WA, and CS), leaving six events as outliers (OT). The objective K-means algorithm separated the 81 events into four groups (OG1, OG2, OG3, and OG4). The hybrid scheme used the 75 events in the four manual principal categories to initialize the K-means algorithm and, then, separated the 81 events into four groups (HG1, HG2, HG3, and HG4). Pattern OG1 OG2 OG3 OG4 Total HG1 HG2 HG3 HG4 LL OF CS WA OT Total frontal events (WA and OF), there were only nine events where the moisture sources can be traced to the subtropical area near Hawaii. However, six out of these nine events were related to extreme snow events over EVI, indicating that moisture origin is an essential factor for a Pacific frontal event. Since a weather system with a moisture origin from the subtropical area is typically associated with stronger warm-air advection, precipitation typing can be a huge challenge for meteorologists (Geng et al. 2012). b. The arctic outflow Arctic outflow is considered to be an important factor for snowstorm development along the west coast of North America (Ferber et al. 1993). A typical synoptic configuration of arctic outflow is a strong high pressure system developing over northern Canada. The cold arctic air is driven into the BC interior. The resulting strong pressure gradient perpendicular to the barrier of the Coast Mountains produces strong outflow winds through coastal inlets and valleys (Jackson and Steyn 1994). As the cold outflow winds blow across the Georgia Strait toward EVI, they can either trigger locally heavy snow events under favorable orographic conditions, or delay the process of snow changing to rain due to warmair advection from the Pacific Ocean (Lange 1998). For a Pacific frontal system with moisture origin from the subtropical area, the easterly outflow is the essential condition for heavy snow over EVI. The intensity of the arctic outflow along the BC south coast can be measured by the northerly wind speed reported from the autostation at Pam Rocks (CWAS; see Fig. 1). This station was purposely installed at the mouth of a major fjord to measure inflow and outflow. As a rule of thumb, a sustained northerly wind of 25 kt or higher at CWAS can be considered a significant outflow. Our analysis indicates that 42 of the 81 snowfall events observed during in EVI were accompanied by significant outflows. The averaged maximum sustained wind speed at CWAS for these 42 events was 37 kt. Among these 42 events, 18 of them were classified as LL events, 10 as OF events, 8 as WA events, 5 as CS events, and 1 as an OT event. Tatar (2006) obtained a linear relation to estimate the maximum sustained outflow wind speed (V,kt)at CWAS from the pressure difference between the Squamish Airport (CWSK) and CWAS (PDIF, hpa): V 5 7: PDIF 1 8:3324. (2) This regression equation, which is valid for V $ 5 kt, is useful when the wind measure at CWAS is not available or reliable. c. Surface low track Our analysis indicates that most of the 81 EVI snowstorms were accompanied by a surface low pressure system in the vicinity of Vancouver Island. Most of the lows with the WA pattern formed at the lower latitudes over the North Pacific and moved northeastward to make their landfall on the BC central coast. The lows with the OF pattern were initiated over diverse areas of the North Pacific, and most of them remained offshore when the occluded front moved across EVI. As mentioned earlier, the northeasterly arctic outflow can trigger a lee low off West Vancouver Island under favorable conditions. Ferber et al. (1993) also discussed the possible mechanisms leading to west coast cyclogenesis along the Washington coast. Based on PSPC surface analysis charts available each 6 h, we plotted surface low tracks for the 13 major or extreme snowfall events in the LL category in Fig. 12. The chart indicates most lee lows were formed locally west of the coastal higher terrain and moved southward along the coastline. d. The GSCZ Operational meteorologists at PSPC noticed the frequent occurrence of a GSCZ and its significant contributions to the local weather (Tatar 2006). A similar convergence zone in Puget Sound has also been identified (Mass 1981; Ferber et al. 1993). In this study, the contribution of the GSCZ to snowstorm development in EVI is investigated using routine weather observations and high-resolution NWP model (e.g., LAM2.5) data. It is found that a convergence zone can form as a result of flow deceleration due to terrain blocking or interaction of the flows from different directions. It may also be induced by the troughing in the lee of Vancouver Island. The presence of the GSCZ significantly increases the efficiency of precipitation. Most of the WA events are accompanied by a surface low tracking toward the BC central coast. As the low

15 OCTOBER 2013 WU ET AL FIG. 11. As in Fig. 10, but for the snowfall events associated with the HG1 (29 cases), HG2 (19 cases), HG3 (19 cases), and HG4 (14 cases) groups from the hybrid K-means clustering procedure. moves to north of Port Hardy (CYZT in Fig. 1), it induces strong southeast winds in the Georgia Strait. The deceleration of these winds due to terrain-blocking effects results in a convergence zone over northern EVI. For the LL events, the northeasterly outflow winds from the mainland inlets and valleys converge with the lowinduced southeast winds from Puget Sound over the southern Georgia Strait, giving locally heavy snowfall over southern EVI. e. Instability When dry arctic air from the BC interior or Alaska moves over the warmer Pacific waters, a destabilization process occurs due to cooling aloft and the vertical transfer of sensible heat and moisture from warmer ocean water. The intensity associated with this destabilization process can be measured by the air sea temperature difference (ASTD). In this study, the air temperature is

16 1234 W E A T H E R A N D F O R E C A S T I N G VOLUME 28 temperatures measured at the West Sea Otter buoy and the La Perouse buoy. Our analysis indicated that the average ASTDs were 17.48C for the 19 LL events and 13.08C for the 15 CS events. The cold arctic air with the CS events came from Alaska and had traveled through a much longer track over the ocean water, resulting in lower ASTD as compared to its LL counterpart. 5. Conceptual models FIG. 12. Surface low tracks of the six major (thin lines) and seven extreme (thick lines) events associated with the lee low pattern. The time interval between two dots is 6 h. The end of the each track is marked by a red dot. calculated as the average of 850-hPa temperatures measured by the Port Hardy sounding and the Quillayute sounding (see Fig. 1 for locations), and the water temperature is calculated as the average of sea surface Based on the above analyses of weather patterns and key ingredients associated with the snowstorms in EVI, four conceptual models were developed to help forecasters understand the physical processes leading to heavy snow in EVI. Schematic diagrams for these conceptual models are shown in Figs. 13 and 14. a. The WA pattern A WA event usually produces widespread precipitation over much of southern BC, but EVI is the most common region for heavy snowfall events. With an arctic front lying parallel to the BC coastline, a Pacific frontal system approaches EVI from the southwest (Fig. 13a). The WA ahead of the cold front transports relatively FIG. 13. Schematic diagrams for heavy snowfalls in EVI with the four conceptual models. The orange double-dashed line represents the trough at the 500-hPa level.

17 OCTOBER 2013 W U E T A L FIG. 14. A schematic of an east west cross section for a heavy snowfall event with the warm advection pattern in EVI. warm, moist air toward Vancouver Island. The ascending WA produces a broad stratiform cloud ahead of the surface warm front, bringing widespread snow to the BC south coast. The PVA ahead of the 500-hPa trough enhances the surface low as it moves toward the BC central coast. In the case of a stronger WA with sufficient moisture originating from the subtropical area, the presence of cold northeasterly outflow winds across the Georgia Strait is crucial for heavy snowfall events in EVI. These outflow winds can be maintained by the cold arctic air residing in the BC interior. Otherwise, the WA will produce rain instead of snow in EVI. With the surface low approaching the BC north coast, the pressure gradient over the Georgia Strait shifts from northeast southwest to southeast northwest. The resulting southeasterly winds in the Georgia Strait are blocked by the terrain over northern EVI, where a convergence zone is created. This convergence adds additional upward vertical motion, which will tend to overcome the subsidence from a southwest flow aloft over the Vancouver Island Ranges (Fig. 14). b. The OF pattern An occluded front moving across Vancouver Island is often associated with an upper-level jet located to the south of the island. Unlike the widespread snow produced by a WA pattern, heavy snow from an OF pattern is typically confined within a narrow precipitation band associated with the OF. Figure 13b shows a schematic diagram for the OF pattern. A Pacific cyclone vertically aligned with a 500-hPa trough has entered its mature stage. The closed circulation usually extends vertically from the surface to the 700-hPa level. This low-level circulation pushes an occluded front across Vancouver Island, leading to heavy snow over EVI. The OF often becomes negatively titled as it move over Vancouver Island. Extreme snow accumulations over EVI are likely to be observed when the narrow precipitation band becomes perpendicular to the island. c. The CS pattern A CS-pattern snow event in EVI typically occurs in the wake of a Pacific frontal system, with a 500-hPa trough lying along the coastline. This kind of snowstorm is most likely to be missed in the forecast due to the convective and localized nature of the snowfall. In the wake of the frontal system, northwesterly flow directs dry arctic air from Alaska through the North Pacific toward the BC and Washington coasts (Fig. 13c). Cooling aloft and the vertical transfer of sensible heat from warm ocean water destabilize the air mass. Numerous convective cells over the Pacific Ocean can be seen in the satellite imagery (Fig. 3c). Some of these cells can produce localized showers or flurries as they move across Vancouver Island. If there is a positive vorticity maximum ahead of the short-wave trough at the 500-hPa level, the maximum of PVA can organize isolated cells into a mesoscale convective cloud band known as a PVA lobe (Johnson and Mortimer 1984), which can turn into a powerful snow maker in EVI. More than half of the 15 CS events were associated with a PVA lobe. d. The LL pattern The LL events are associated with a strong ridge over the North Pacific and a deep trough over western Canada at the 500-hPa level (see Figs. 9a and 11a). The main features of this pattern are illustrated in Fig. 13d. Northeasterly flow ahead of the upper ridge drives cold arctic air from northern Canada to the BC interior. A lee low off Vancouver Island or the Washington coast is forced by the persistent northeast flow crossing the Vancouver Island Ranges or the Olympic Mountains (Ferber et al. 1993). The cold-air damming along the eastern slopes of the Coast Mountains also forces lowlevel outflow winds through the coastal inlets and valleys into the Georgia Strait (Jackson and Steyn 1994). Locally, heavy snow can be produced by these outflow winds alone as they blow onshore toward EVI. The deepening of the lee low further enhances the northeasterly outflow in the Georgia Strait. Meanwhile, the corresponding low-level cyclonic circulation advects moist marine air through the Juan de Fuca Strait and Puget Sound into the Georgia Strait, where a convergence zone (i.e., GSCZ) is created as the southeast winds associated with the lee low encounter the northeast outflow winds. A convergence zone can also form as the result of southeast or northeast winds in the Georgia Strait being blocked by the higher terrain of EVI. The presence of a convergence zone usually yields heavy snow in the southern section of EVI. 6. Preliminary evaluation The classification systems and conceptual models discussed in the previous sections were based on the data of EVI snowstorms in the 10-yr period from 2000 to

18 1236 W E A T H E R A N D F O R E C A S T I N G VOLUME 28 TABLE 3. Information on the 10 snowfall events that occurred in the winter season of 2010/11. The warning status (WS) and warning lead time (WLT) are given in the sixth and seventh columns, respectively. The last three columns list the class memberships of each event based on the manual, hybrid, and objective classification systems; see Table 2 for group name convention. Case Date Location Observed snowfall (cm) Predicted snowfall (cm) WS WLT (h) Membership 1 20 Nov 2010 S. EVI Hit 14 LL HG1 OG Nov 2010 S. EVI Hit 2 LL HG1 OG Nov 2010 C. EVI Hit 26 WA HG4 OG Dec 2010 N. EVI 8 10 Hit 14 CS HG3 OG4 5 9 Jan 2011 C. EVI Hit 18 LL HG1 OG Jan 2011 EVI Hit 30 OF HG1 OG Feb 2011 C. EVI 10 Miss CS HG3 OG Feb 2011 N. EVI Hit 1 LL HG3 OG Feb 2011 S. EVI Hit 3 CS HG1 OG Mar 2011 N. EVI Hit 2 WA HG1 OG In this section, 10 snowfall events (1 extreme, 5 major, and 4 regular) that occurred in EVI during the winter season of 2010/11 are used to evaluate these conceptual models. The main features of these events, together with their group memberships determined from manual, objective, and hybrid classification systems, are listed in Table 3. Through this winter season, 10 snowfall warnings were issued by PSPC for the EVI region. One of these warnings was a false alarm. For the nine hit events, the snow amount was underforecast by about 50% on average. Note that 8 of the 10 events were forecast as regular events (5 10 cm). Forecasters were not confident enough to predict major and extreme snow events in EVI. The corresponding CSI obtained from Eq. (1) is 0.82, higher than the CSI of 0.59 for the previous 10-yr period. To evaluate the potential of the four conceptual models developed in the previous section, we used the NWP products of the operational GEM-R (15-km resolution) model and the high-resolution LAM2.5 model to classify the weather patterns and examine the weather ingredients of the 10 snowfall events. The manual classification identified four LL events, three CS events, two WA events, and one OF event. With this pattern recognition technique, forecasters would be able to issue snowfall warnings much earlier for most of these events, and would not miss the event on 17 February 2011 (No. 7 in Table 3). Further ingredient analysis would convince the forecasters to predict snowfall amounts of cm (major events) for most of the cases. Figure 15 shows the 48-h forecast charts of the GEM-R model and the 20- and 24-h forecast charts of the LAM2.5 model for the snowfall event on 20 November 2010 (No. 1 in Table 3). The predicted 500-hPa pattern in Fig. 15a and the sea level pattern in Fig. 15b, valid at 0600 UTC 20 November 2010, strongly suggest that an LL pattern would occur. The high-resolution model predicted strong outflow blowing from the coastal valleys into the Georgia Strait and toward EVI (Fig. 15c). These outflow winds, which reach kt near the Pam Rocks station (CWAS), would create a strong convergence zone in the southern section of EVI. The LAM2.5 model only predicted 12-h ( UTC) precipitation amounts of 5 8 mm in the southern section of EVI (Fig. 15d). This would typically be interpreted as 5 10 cm of snowfall by operational meteorologists. However, further analysis based on the NWP output indicates that four key ingredients (i.e., the water vapor supply, instability, arctic outflow, and Georgia Strait convergence zone) would be associated with this system. Therefore, a major to extreme snowfall event should be forecast for southern EVI, with snowfall amounts of cm or higher expected. Figure 16 shows the surface analysis at 0600 UTC and some satellite and radar images at 0800 UTC 20 November The mean sea level pressure at the lee low center off Vancouver Island was 998 hpa (Fig. 16a), exactly as predicted by the GEM-R model with a 48-h lead time (Fig. 15b). The radar image at 0800 UTC indicates the presence of GSCZ across the southern section of EVI, with snow rates as high as 8 cm h 21 in the Shawnigan Lake area (Fig. 16c). The radar also detected a maximum echo top of 6 km in this area (Fig. 16d), suggesting the presence of low-level instability. At the end of the storm, a snowfall amount of 29 cm was reported at the Shawnigan Lake area, while only 2 cm was recorded at Comox in the central section of EVI. As mentioned earlier, 1 of the 10 snowfall warnings issued for EVI in this winter season was a false alarm. The analysis of the false alarm rate from the conceptual models would require detailed evaluation of the models through the whole season. This could be a subject of future study. As pointed out by one of the reviewers, many winter storms that cross the west coast of Canada

19 OCTOBER 2013 WU ET AL FIG. 15. NWP charts for the snowfall event on 20 Nov (a) Canadian GEM-R model (15-km resolution) 48-h forecast of 500-hPa geopotential height (solid lines at 6-dam intervals) and absolute vorticity (dashed lines at s21 intervals), valid at 0600 UTC. (b) As in (a), but for mean sea level pressure (solid lines at 4-hPa intervals) and hPa thickness (dashed lines at 6-dam intervals). (c) High-resolution LAM2.5 model 20-h forecast of near surface winds (kt), valid at 0800 UTC. (d) As in (c), but for 12-h precipitation accumulation (mm) valid at 1200 UTC. would bear resemblance to the principal patterns identified in this study, but only a few of them would produce snow in EVI. Therefore, some criteria should be used to discriminate the no snow storms from the snowstorms. A rule of thumb in operational forecasting is to predict snow only when the surface temperature is expected to fall below 48C during a precipitation event. 7. Conclusions The complex terrain in East Vancouver Island of British Columbia leads to frequent heavy snowfall events in winter. Snowstorm development involves complicated interactions among the atmosphere, ocean, and local topography, and presents a great challenge to operational meteorologists. To improve our understanding of, and

20 1238 WEATHER AND FORECASTING VOLUME 28 FIG. 16. Weather observations of the snowfall event on 20 Nov (a) Surface analysis of PSPC, valid at 0600 UTC. (b) Satellite imagery [Geostationary Operational Environmental Satellite-11 (GOES-11), IR] at 0800 UTC. (c) Reflectivity (dbz) and snow rate (cm h21) from CWUJ, valid at 0800 UTC. (d) As in (c), but for the height of the echo top. ability to predict, heavy snowfall events in EVI, we performed a manual pattern recognition analysis and a key ingredient investigation on the 81 snowfall events that occurred during the 10-yr period from 2000 to It is shown that the conditions for the snowfall events in EVI can be classified into four principal categories. The most common pattern is characterized by an occluded front moving across EVI (OF pattern). Meanwhile, most of the extreme snow events in EVI are associated with a lee low off Vancouver Island or the Washington coast, which is induced by strong arctic outflow across the coastal mountains (LL pattern). A warm advection (WA) pattern is generally benign due to the quick snow-to-rain changeover, but heavy snowfalls may occur especially over northern EVI in the presence of cold northeasterly outflow winds across the Georgia Strait. Banded convection in an unstable air mass (CS pattern) can produce locally heavy snowfall events in