Heavy Snow Events Research Articles

Raindrop Size Distribution Characteristics of Heavy Precipitation Events Based on a PWS100 Disdrometer in the Alpine Mountains, Eastern Tianshan, China

As a key component of the hydrological cycle, knowledge and comprehension of precipitation formation and evolution are of leading significance. This study investigates the statistical characteristics of raindrop size distribution for heavy precipitation events with observations collected by a Present Weather Sensor (PWS100) disdrometer located in the alpine area of eastern Tianshan, China. The characteristics are quantified based on heavy rain, heavy snow, and hail precipitation events classified using the rainfall intensity and the precipitation-related weather codes (US National Weather Service). On average, the heavy precipitation events in the headwaters of the Urumqi River are dominated by medium-sized (2–4 mm) raindrops. As well, we investigate mass-weighted mean diameter–normalized intercept parameter scatterplots, which demonstrate that the heavy precipitation events in alpine regions of the Tianshan Mountains can be identified as maritime-like clusters. The concentration of raindrops in heavy precipitation is the highest overall, while the concentration of raindrops in heavy snow is the lowest when the diameter is lower than 1.3 mm. The power–law relationships of radar reflectivity (Z) and rain rate (R) [Z = ARb] for the heavy rain, heavy snow, and hail precipitation events are also calculated. The Z–R relationship of heavy rain and heavy snow in this work has a lower coefficient value of A (10 and 228.7, respectively) and a higher index value of b (2.6 and 2.1, respectively), and the hail events are the opposite (A = 551.5, b = 1.3), compared to the empirical relation (Z = 300R1.4). Furthermore, the possible thermodynamics and general atmospheric circulation that cause the distinctions in the raindrop size distribution characteristics between alpine areas and other parts of the Tianshan Mountains are also debated in this work. The headwaters of the Urumqi River in alpine areas have relatively colder and wetter surroundings in the near-surface layer than the foothills of the Tianshan Mountains during the precipitation process. Meanwhile, a lower temperature, a higher relative humidity, a more efficient collision coalescence mechanism, and glacier local microclimate effects (temperature jump, inverse glacier temperature, glacier wind) at the headwaters of the Urumqi River during the precipitation process are probably partly responsible for more medium- and large-size drops in the mountains.

Synoptic analysis of the January 2004 snowstorm: Example of Çanakkale

Many natural disasters have occurred on the Earth we live in. Humans have always been in a struggle with these natural phenomena. Today, people are still struggling with atmospheric extreme events. One of the most important disasters like this is snowstorms that occur suddenly in winter. Especially countries in the middle belt (30-60 latitudes in the Northern Hemisphere) are constantly affected by snowstorms. Turkey, located in the Middle Belt, is also exposed to these storms during the winter season. From this perspective, the analysis of synoptic conditions and weather forecast is an important factor for risk analysis and take precautions for winter storms. Thus, the predictions made with the tracking of air masses and synoptic analysis make it easier to understand how the storm occurred. For this purpose, synoptic analyses were conducted to understand the development of the snowstorm that occurred in the high atmosphere in Çanakkale on January 20-30, 2004. This study examined how weather conditions change. Radiosonde data, surface maps, and daily bulletins provided from the Turkish State Meteorological Service were used in the study. Archival data were used to track air mass orbits using the Hysplit trajectory method. Stability and instability assessments of air masses were evaluated using the data from radiosonde stations using the K Index. Spatial analysis software was used to analyze the data and show the thematic distribution of topography. According to the findings, during the passage of a cold front of air masses coming from the north to Çanakkale on January 22, gusty winds began, suddenly dropped the temperatures, and began heavy snow events. Later, it turned into a blizzard and formed a type of snow.

Macro- and microphysical characteristics of snowfall and non-snowfall clouds in the West Tianshan Mountains of China based on cloud radar

The macro- and microphysical characteristics of wintertime precipitating clouds and non-precipitating clouds over the West Tianshan Mountains, China, were analyzed with the use of Ka-band radar and weighing rain gauge observations. The data were collected from January to February 2019, December 2019, and from December 2020 to February 2021. Snowfall clouds mainly ranged from 0.15 ~ 2.50 km and had a reflectivity (Z) of mostly 10 ~ 33 dBZ. Non-snowfall clouds were primarily distributed within the height range of 2 ~ 8 km, and the Z values were within the range of − 22 ~ 15 dBZ. Compared with non-snowfall clouds, snowfall clouds have a higher particle water content (M) but a similar radial velocity (V). Light and moderate snowfall clouds were mainly located at heights of 0.15 ~ 3.50 km and had Z values concentrated from 5 ~ 24 dBZ. Heavy snowfall clouds were characterized by a Z of 5 ~ 30 dBZ below 3.5 km. The proportion of clouds with an M value > 0.1 g·m−3 below 2 km was noticeably higher for heavy snow events than for light and moderate snow events. The differences in the distributions and values of snowfall cloud V values were small among the different snow types, and descending motions occurred below 6 km, with V ranging − 1.4 ~ − 0.3 m·s−1. The heights of the non-snowfall cloud top and base during the day were lower than those at night. The snowfall cloud top did not show noticeable diurnal variations. The cloud top and base heights of the non-snowfall clouds both showed a single-peak distribution. The cloud top values of snowfall clouds exhibited bimodal distributions.

Incorporating weather variables with probabilistic approach for trip planning

Adverse weather conditions are known to negatively impact traffic operations by creating unsafe road conditions, slowing vehicles, and causing congestion – events that subsequently lead to extra costs and travel time delay if they are not considered explicitly in trip planning. This study proposes an approach that considers incorporates adverse weather conditions into trip planning modeling. As such, speed data from Weigh-in-Motion (WIM) sensors along with weather information from multi-region within the State of Ohio were used. To assess departure time and route choice under varying levels of snowstorm events, a probabilistic model was developed. Travel time probability is used to quantify the impact of weather events on travelers' decisions regarding both route choice and departure time. As expected, the results showed that heavy snow associated with low visibility has the most impact on driver speed behavior. A measure of driver's speed variability associated with different weather conditions is provided for the collected data from the studied regions in Ohio. Moreover, some drivers showed hesitation during the storm and dropped their speed to as low as 20 mph. In addition, the vehicle speed performance during the events was used to calculate the expected time throughout each event. Simulations on a road network were used to compare the expected total travel time and the variance along with the probability of paths under a heavy snowstorm. Results indicated that, under adverse weather conditions, drivers' route choice is not based on the shortest travel time but on the decision of alternate routes being attractive (safe, snow cleared, etc.). Giving the additional trip information of these events, decision-makers should pay more attention before deciding the departure and route choice on heavy snow events.

Quantifying sedimentation patterns of small landslide‐dammed lakes in the central Oregon Coast Range

AbstractUnderstanding sedimentation patterns in small coastal watersheds due to landscape perturbations is critical for connecting hillslope and fluvial processes, in addition to managing aquatic habitats for anadromous fish and other aquatic species in the Oregon Coast Range (OCR). Changes in sedimentation patterns spanning the last 250 years are preserved in two landslide‐dammed lakes in small watersheds (< 10 km2) underlain by the Tyee Formation in the central OCR. Dendrochronology of drowned Douglas‐fir stumps in both lakes provided precise timing of the damming and formation of the lakes, with Klickitat Lake forming in winter ad 1751/52 and Wasson Lake in winter ad 1819/20. Perturbations from wildfires, logging and road development, and previously underappreciated snow events affect sedimentation rates in the lakes to different degrees, and are identified in the sediment record using cesium‐137 (137Cs), high‐resolution charcoal stratigraphy, local fire records, and aerial photography. Each lake has variable sedimentation accumulation rates (0.05–4.4 cm yr−1) and mass accumulation rates (0.02–1.42 g cm−2 yr−1). Sedimentation rates remained low from the landslide‐damming events until the mid‐19th century, when they increased following stand‐replacing wildfires. Aside from a sediment remobilization triggered by human modification of the landslide dam at Klickitat Lake around 1960, the largest peaks in mass accumulation rates in the mid‐20th century at both lakes in the early 1950s precede major road construction and logging activity in the watersheds. Subsequent sedimentation rates are lower, but variable, and possible effects of logging and road development might be exacerbated by abnormal precipitation and heavy snow events. A comparison of previous studies of landslide‐dammed lakes in larger watershed of the OCR are consistent with our findings of increased sedimentation in the mid‐20th century, as well as higher sedimentation rates in the debris‐flow dominated southern Tyee Formation than in the lower‐relief northern Tyee Formation.

Dealing with climate change and other stressors: small-scale coffee farmers in the Fero-two Peasant Association in the Wensho district, southern Ethiopia

Drawing on evidence from in-depth interviews and a household survey, the paper provides evidence on the farmers’ challenges in production, marketing and prices as well as how farmers perceive and deal with climate change impacts on coffee production in the Fero-two Peasant Association (PA) of the Wensho district of the Sidama region in southern Ethiopia. This includes prolonged dry season, erratic and insufficient rainfall, too early and too late rains and temperature rise. Unexpected heavy rains and snow events as well as weather change-driven pests and diseases negatively affect not only the volume of coffee production and quality but also the prices. The effects of climate change risks add pressure on the existing challenges such as land shortage and fragmentation, declining soil fertility and weeds. Lack of participation in decision-making and corruption are additional challenges for the coffee growers. Although the farmers rely on multiple production-related adaptation responses, most of the deployed strategies to the changing environment are less effective and short-term. The paper concluded that policy interventions should better comprehend the local vulnerability to build resilience and to undertake sustainable agricultural practices for improving the farmers’ adaptive capacity to deal with the effects of climate change.

Global Distribution of Snow Precipitation Features and Their Properties from 3 Years of GPM Observations

Abstract Using a 3-yr Global Precipitation Mission (GPM) Ku-band Precipitation Radar (KuPR) dataset, snow features (SFs) are defined by grouping the contiguous area of nonzero solid precipitation. The near-surface wet bulb temperatures calculated from ERA-Interim reanalysis data are used to verify that SFs are colder than 1°C to omit snowfall that melts before reaching the surface. The properties of SFs are summarized to understand the global distribution and characteristics of snow systems. The seasonal and diurnal variations of SFs and their properties are analyzed over Northern and Southern Hemispheric land and ocean separately. To quantify the amount of snow missed by the GPM KuPR and the amount of snow underestimated by the CloudSat Cloud Profiling (CPR), 3-yr KuPR pixel-level data are compared with 4-yr CloudSat CPR observations. The overall underestimation of snowfall during heavy snow events by CPR is less than 3% compared to the combined CPR and KuPR estimates. KuPR underestimates about 52% of weak snow. Only a small percentage of SFs have sizes greater than 10 000 km2 (0.35%), maximum near-surface reflectivity above 30 dBZ (5.1%), or echo top above 5 km (1.6%); however, they contribute 40%, 49.5%, or 30.4% of the global volumetric snow detected by KuPR. Snow in the Northern Hemisphere has stronger diurnal and seasonal variation compared to the Southern Hemisphere. Most of the SFs over the ocean are found with relatively smaller, less intense, and shallower echo tops than over land.

When do extreme weather events generate attention to climate change?

We analyzed the effects of 10,748 weather events on attention to climate change between December 2011 and November 2014 in local areas across the USA. Attention was gauged by quantifying the relative increase in Twitter messages about climate change in the local area around the time of each event. Coastal floods, droughts, wildfires, strong wind, hail, excessive heat, extreme cold, and heavy snow events all had detectable effects. Attention was reliably higher directly after events began, compared to directly before. Financial damage associated with the weather events had a positive and significant effect on attention, although the effect was small. The abnormality of each weather event’s occurrence compared to local historical activity was also a significant predictor. In particular and in line with past research, relative abnormalities in temperature (local warming) generated attention to climate change. In contrast, wind speed was predictive of attention to climate change in absolute levels. These results can be useful to predict short-term attention to climate change for strategic climate communications and to better forecast long-term climate policy support.

Comparison of the GPCP 1DD Precipitation Product and NEXRAD Q2 Precipitation Estimates over the Continental United States

Abstract This study compares the Global Precipitation Climatology Project (GPCP) 1 Degree Daily (1DD) precipitation estimates over the continental United States (CONUS) with National Mosaic and Multi-Sensor Quantitative Precipitation Estimation (NMQ) Next Generation (Q2) estimates. Spatial averages of monthly and yearly accumulated precipitation were computed based on daily estimates from six selected regions during the period 2010–12. Both Q2 and GPCP daily precipitation estimates show that precipitation amounts over southern regions (<40°N) are generally larger than northern regions (≥40°N). Correlation coefficients for daily estimates over selected regions range from 0.355 to 0.516 with mean differences (GPCP − Q2) varying from −0.86 to 0.99 mm. Better agreements are found in monthly estimates with the correlations varying from 0.635 to 0.787. For spatially averaged precipitation values averaged from grid boxes within selected regions, GPCP and Q2 estimates are well correlated, especially for monthly accumulated precipitation, with strong correlations ranging from 0.903 to 0.954. The comparisons between two datasets are also conducted for warm (April–September) and cold (October–March) seasons. During the warm season, GPCP estimates are 9.7% less than Q2 estimates, while during the cold season GPCP estimates exceed Q2 estimates by 6.9%. For precipitation over the CONUS, although annual means are close (978.54 for Q2 vs 941.79 mm for GPCP), Q2 estimates are much larger than GPCP over the central and southern United States and less than GPCP estimates in the northeastern United States. These results suggest that Q2 may have difficulties accurately estimating heavy rain and snow events, while GPCP may have an inability to capture some intense precipitation events, which warrants further investigation.

Do Extreme Weather Events Generate Attention to Climate Change?

We analyzed the effects of 10,748 weather events on attention to climate change between December 2011 and November 2014 in local areas across the United States. Attention was gauged by quantifying the relative increase in Twitter messages about climate change in the local area around the time of each event. Coastal floods, droughts, wildfires, strong wind, hail, excessive heat, extreme cold, and heavy snow events all had detectable effects. Attention was reliably higher directly after events began, compared to directly before. This suggests that actual experiences with extreme weather events are driving the increases in attention to climate change, beyond the purely descriptive information provided by the weather forecasts directly beforehand. Financial damage associated with the weather events had a positive and significant effect on attention, although the effect was small. The abnormality of each weather event’s occurrence compared to local historical activity was also a significant predictor. In particular and in line with past research, relative abnormalities in temperature (“local warming”) generated attention to climate change. In contrast, wind speed was predictive of attention to climate change in absolute levels. These results can be useful to predict short-term attention to climate change for strategic climate communications, and to better forecast long-term climate policy support.

종관기압 패턴별로 분석한 강원 지역 기상재해

강원도 지역은 태백산맥과 동해의 복합적인 영향으로 다른 지역에 비해 재해기상이 빈번히 발생하는 지역으로, 이 지역에서 발생하는 재해기상(호우, 대설, 태풍, 강풍 등)에 의한 피해액은 우리나라 전체 피해액의 1/3정도를 차지한다. 본 연구에서는 소방방재청에서 제공하는 2001년부터 2010년까지의 '재해연보'의 내용을 바탕으로 호우, 대설, 강풍에 의하여 강원도 지역에서 재해가 발생한 사례들을 추출하였고, 그 사례들을 종관 기압 패턴에 따라 분류하여 유형별 재해 피해액을 조사하여 재해 취약지역을 밝혀내었다. 강원도 지역에서는 10년 동안에 총 32 건의 호우에 의한 재해 사례가 발생하였고, 호우에 의한 강원도 지역의 총 재해 피해액은 2조 2,027억 34백만 원으로 집계되었다. 특히, 평창군과 인제군 지역에서는 각각 6,036억 8백만 원, 5,789억 2백만 원 정도로 큰 피해를 입었다. 호우에 의한 재해 사례를 종관 기압 패턴에 따라 분류하여 재해 피해액을 살펴보면, 저기압 통과형의 경우 3,025억 7백만 원 정도이었고, 장마전선형의 경우에는 1조 9,002억 13백만 원 정도이었다. 즉 호우의 경우, 장마전선형 호우에 의한 재해 피해가 가장 많았고, 호우와 관련된 재해 취약 지역은 평창군과 인제군 지역으로 밝혀졌으며, 이들 지역에 대해서 호우 예방 및 대책 지원의 우선순위를 둘 필요가 있다. 대설에 의한 재해 사례의 경우, 재해 사례 건수는 2001년부터 2010년까지 총 8 건이었고, 대설에 의한 강원도 지역의 총 재해 피해액은 238억 51백만 원 정도로 집계되었다. 특히, 홍천군은 49억 63백만 원 정도, 춘천 지역은 44억 11백만 원 정도의 피해를 입었다. 대설에 의한 재해 사례를 종관 기압 패턴에 따라 분류하여 재해 피해액을 살펴보면, cP 고기압 확장형 대설의 경우에는 41억 79백만 원 정도, 저기압 이동형 대설의 경우에는 196억 72백만 원 정도이어서, 저기압 이동형 대설의 경우가 cP 고기압 확장형 대설의 경우보다 더 큰 재해를 일으켰음을 알 수 있다. 마지막으로 강풍에 의한 재해 사례의 경우, 10년 동안에 재해 사례 건수는 총 2 건이었고, 강원 영동 해안 지역에 위치한 삼척과 강릉 지역에서 각각 15백만 원, 63백만 원 정도의 피해가 나타나, 강풍의 피해지역은 강원 영동 지역으로 국한되었다. Based on the contents of 'yearbook of calamities' from 2001 to 2010 provided by the National Emergency Management Agency, meteorological disasters, generated by heavy rain, heavy snow and strong wind in the Gangwon-do region, were classified in accordance with synoptic pressure pattern to survey the damage of disaster with the pressure pattern types and to reveal regions vulnerable to disasters. In Gangwon-do region, total 32 events of heavy rain occurred for ten years, which lead to about 2,203 billion won of the total damage amount. Especially, Pyeongchang-gun and Inje-gun had outstanding damages of about 604 billion won and 579 billion won, respectively. In case of migratory low type, the amount of disaster damage caused by heavy rain was about 303 billion won while there was damage of about 1,900 billion won in case of Changma front type. The amount of disaster caused by Changma front type was six times more than that caused by migratory low type. Since regions vulnerable to disasters from this survey were Pyeongchang-gun and Inje-gun, these regions would need to have priority to heavy rain prevention and measurement aid. The total damage amount caused by the heavy snow events was 24 billion won in Gangwon-do region. Especially, Hongcheon-gun and Chuncheon-si had damage amount of about 4.9 billion won and 4.4 billion won, respectively. In case of cP high air pressure expansion type, the disaster by the heavy snow events caused the damage of about 4.1 billion won while the damage amount of 19.6 billion won was mainly caused by the migratory low type. Finally, there were two strong wind events for 10 years, leading to the damage of about 15 million won and 63 million won in Samcheok-si and Gangneung-si located in the Yeongdong coastal region, respectively.

Significant Winter Weather Events and Associated Socioeconomic Impacts (Federal Aid Expenditures) across Oklahoma: 2000–10

Abstract Exceptionally severe winter storms that overwhelm local government result in major disaster declarations. Each National Weather Service forecast office in the United States reports winter events for a specific group of counties, known as the county warning area. Such events are reported as blizzard, ice storm, winter storm, heavy snow, or winter weather. They are archived by the National Climatic Data Center and are published in Storm Data, a monthly periodical. Using Storm Data, all winter reports in Oklahoma from 1 November 1999 to 1 May 2010 were compiled into a database. The results of this study demonstrated that while counties in northern Oklahoma received the highest number of winter reports, when compared with climatology winter storm, heavy snow, ice storm, and blizzard storm types yielded an above-average occurrence across much of southwest and central Oklahoma over the study period. Disaster information, obtained from the Federal Emergency Management Agency, showed that from 1 November 1999 to 1 May 2010 Oklahoma led the nation with nine winter-related federal disasters, resulting in nearly $800 million (U.S. dollars) in federal aid. High-impact events, determined by disaster declarations, were most frequent across southwest, central, and northeast Oklahoma, and southwest Oklahoma experienced a disaster nearly every third winter event. Over much of Oklahoma, ice storms were more likely to result in a disaster than winter storm, blizzard, and heavy snow events combined. Spatial distributions of federal aid showed that rural counties were most impacted by the winter weather disasters and required greater federal assistance.

Characteristics of the top ten snowstorms at First-Order Stations in the U.S.

Snowstorms can produce varying degrees of damage depending on the amount and intensity of the snowfall over a given amount of time. Concurrent weather conditions such as freezing rain and high winds often exacerbate the amount of damage received. In order to assess the frequency of potentially damaging conditions during climatologically significant snowstorms, the top ten snowstorms (TTS) at individual First-Order Stations in the eastern two-thirds of the conterminous U.S. were determined, and the hourly weather conditions during each event were analyzed. The results show that TTS have occurred as early as September and as late as June, with January being the peak month of occurrence. Hourly precipitation totals during TTS were 2.3 mm or less 88% of the time. Seven percent of TTS were classified as a blizzard with over half of the blizzards occurring in the West North Central region. The most common concurrent weather condition during a TTS was fog followed by blowing snow. Regionally, heavy snow events in the Northeast had relatively higher precipitation amounts, colder temperatures, higher winds, and more fog and blowing snow than any other region.

Risk evaluation of heavy snow disasters using BP artificial neural network: the case of Xilingol in Inner Mongolia

According to disaster and risk evaluation theory, we proposed an indicator system containing environmental possibilities with hazard, disaster inducing factors and disaster bearing bodies to analyze the risk of heavy snow disaster in Xilingol, Inner Mongolia, based on the analysis of heavy snow events that have occurred in the last several decades. A risk evaluation model of heavy snow disaster was established using back-propagation artificial neural network (BP-ANN). Data obtained from a number of heavy snow events samples were used to train artificial neural network (ANN). The objective of this study is to produce a new evaluation model using BP-ANN for heavy snow risk analysis. As a result, BP-ANN model showed an advantage in heavy snow risk evaluation in Xilingol compared to the conventional method of evaluation criteria equation (ECE) introduced by Inner Mongolia Municipality Animal Husbandry Bureau. Thus, the BP-ANN model provides an alternative method for heavy snow risk analysis in the area.

Response of the Indian summer monsoon circulation and rainfall to seasonal snow depth anomaly over Eurasia

Several observational and modeling studies indicate that the Indian summer monsoon rainfall (ISMR) is inversely related to the Eurasian snow extent and depth. The other two important surface boundary conditions which influence the ISMR are the Pacific sea surface temperature (SST) to a large extent and the Indian Ocean SST to some extent. In the present study, observed Soviet snow depth data and Indian rainfall data for the period 1951–1994 have been statistically analyzed and results show that 57% of heavy snow events and 24% of light snow events over west Eurasia are followed by deficient and excess ISMR respectively. Out of all the extreme monsoon years, care has been taken to identify those when Eurasian snow was the most dominant surface forcing to influence ISMR. During the years of high(low) Eurasian snow amounts in spring/winter followed by deficient(excess) ISMR, atmospheric fields such as temperature, wind, geopotential height, velocity potential and stream function based on NCEP/NCAR reanalyses have been examined in detail to study the influence of Eurasian snow on the midlatitude circulation regime and hence on the monsoon circulation. Results show that because of the west Eurasian snow anomalies, the midlatitude circulations in winter through spring show significant changes in the upper and lower level wind, geopotential height, velocity potential and stream function fields. Such changes in the large-scale circulation pattern may be interpreted as precursors to weak/strong monsoon circulation and deficient/excess ISMR. The upper level velocity potential difference fields between the high and low snow years indicate that with the advent of spring, the winter anomalous convergence over the Indian region gradually becomes weaker and gives way to anomalous divergence that persists through the summer monsoon season. Also the upper level anomalous divergence centre shifts from over the Northern Hemisphere and equator to the Southern Hemisphere over the Indian Ocean and Australia.

Rural Intelligent Transportation System Natural-Hazard Management on Low-Volume Roads

The growth of winter travel on alpine roads in the western United States has increased the risk to motorists and highway maintenance personnel owing to a variety of natural hazards. Hazards include snow and ice, avalanching snow, and blowing and drifting snow. The conditions call for attendant need for incident response. A substantial number of affected routes are low-volume rural winter roads. Configurations have been developed for rural intelligent transportation system (ITS) technology that can detect hazards and provide, autonomously and in real time, warnings to and traffic control actions for motorists, highway maintainers, and incident responders for roadway natural hazards. These warnings include on-site traffic control signing and road closure gates, in-vehicle audio alarms for agency maintenance and patrol vehicles, and notification to highway agency maintenance facilities or centralized multiagency dispatchers. These actions and notifications are initiated automatically from the remote rural sites and via manual intervention from off-site personnel, well removed from the rural roadway corridor itself. About 5 years of experience have been accumulated in using these rural ITS natural-hazard reduction systems, including snow avalanche detection and warning systems on Loveland Pass, Colorado; Hoback Canyon, Wyoming; and Banner Summit, Idaho. Automated road closure gates on the Teton Pass in Idaho and Wyoming now allow for remote road closure during heavy snow events. These cost-effective ITS natural-hazard systems are highly exportable for other processes that affect rural low-volume roadways, including landslide, flooding, high surf, high winds, loss of visibility, wildlife, and other natural hazards of this type.

CLIMATOLOGY OF SURFACE CYCLONE TRAJECTORY AND INTENSITY FOR HEAVY-SNOW EVENTS AT THREE MIDWESTERN STATIONS

In this paper, we assess interstorm cyclone variations in intensity and motion for heavy-snow events at three north-central U.S. stations: Minneapolis, Madison, and Indianapolis. Daily data for the period from 1966 to 1996 were used to select the 157 storm events. Heavy-snow cyclone track location (latitude and longitude) and intensity (central pressure) means and standard deviations are calculated for each station and related to snowfall magnitude. The most northwesterly of the three stations, Minneapolis, had the largest median heavy-snowfall total and also the largest standard deviation in heavy-snowfall amount. Statistical analysis of surface cyclone parameters reveals significant interstation differences in storm trajectory and intensity measures for all cyclones and cyclogenesis group subsets. Chi-square tests suggest that the distribution of storms by month and cyclogenesis type is significantly nonrandom for all stations. Summary findings indicate that Midwestern heavy-snow events are dominated by southern Great Plains cyclones and that storms from secondary cyclogenic regions contribute to significant differences at the three selected locations. [Key words: cyclones, snowfall, Midwest.]

The Interaction of Jet Streak Circulations during Heavy Snow Events along the East Coast of the United States

Abstract The interaction of transverse vertical circulations associated with two separate jet steak/trough systems is found to be a common feature of cyclogenetic events which produce heavy snow along the East Coast of the United States. The transverse circulations are identified for eight cases that span the period from 1960 to 1987 utilizing an isentropic analysis of the operational radiosonde data. The analyses depict the interaction of 1) a direct circulation located within the confluent entrance region of an upper-level jet streak over the northeastern United States or southeastern Canada with 2) an indirect circulation in the diffluent exit region of a jet streak associated with a trough nearing the East Coast. This interaction contributes to differential moisture and temperature advections and vertical motions necessary to produce heavy snowfall along the coast. It is suggested that the circulation patterns associated with the jet streak establish an environment within which boundary layer processe...

Use of Enhanced IR/Visible Satellite Imagery to Determine Heavy Snow Areas

Abstract Interpretation techniques are established which relate heavy snow (100 mm or more in a 12 h period) areas to real-time infrared and visible geostationary satellite imagery. An initial collection of cases totaled about 75 during the period from the fall of 1979 to the spring of 1984. The main area of concern extended from the Rockies to the Appalachians. By using conventional surface and upper air data and numerical model output from the LFM-II relationships were established between heavy snow occurrences and cloud patterns in the satellite imagery. The different cloud patterns and flow regimes suggest a classification of heavy snow events into four main types: High Plains cyclogenesis, northwest and southwest flow short-wave troughs, and orographic. Each type has preferred geographical locations, common snowfall durations, storm totals, and band widths and lengths. The snowfall maximum with each storm is influenced by the strength and movement of the system and the structure of the moist layer. S...