National Academies Press: OpenBook

A Guidebook for Airport Winter Operations (2015)

Chapter: Chapter 5 - Historical Winter Storm Event Data

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Suggested Citation:"Chapter 5 - Historical Winter Storm Event Data." National Academies of Sciences, Engineering, and Medicine. 2015. A Guidebook for Airport Winter Operations. Washington, DC: The National Academies Press. doi: 10.17226/22221.
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Page 28
Suggested Citation:"Chapter 5 - Historical Winter Storm Event Data." National Academies of Sciences, Engineering, and Medicine. 2015. A Guidebook for Airport Winter Operations. Washington, DC: The National Academies Press. doi: 10.17226/22221.
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Page 29
Suggested Citation:"Chapter 5 - Historical Winter Storm Event Data." National Academies of Sciences, Engineering, and Medicine. 2015. A Guidebook for Airport Winter Operations. Washington, DC: The National Academies Press. doi: 10.17226/22221.
×
Page 29
Page 30
Suggested Citation:"Chapter 5 - Historical Winter Storm Event Data." National Academies of Sciences, Engineering, and Medicine. 2015. A Guidebook for Airport Winter Operations. Washington, DC: The National Academies Press. doi: 10.17226/22221.
×
Page 30
Page 31
Suggested Citation:"Chapter 5 - Historical Winter Storm Event Data." National Academies of Sciences, Engineering, and Medicine. 2015. A Guidebook for Airport Winter Operations. Washington, DC: The National Academies Press. doi: 10.17226/22221.
×
Page 31
Page 32
Suggested Citation:"Chapter 5 - Historical Winter Storm Event Data." National Academies of Sciences, Engineering, and Medicine. 2015. A Guidebook for Airport Winter Operations. Washington, DC: The National Academies Press. doi: 10.17226/22221.
×
Page 32

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27 Historical Winter Storm Event Data An airport operator’s winter operations capabilities are rarely questioned, except when they are overwhelmed by a significant winter storm event and the impacts are felt by the airport, its stakeholders, and the traveling public. This can occur at airports of any size. At a general avia- tion airport, the impact may involve a single corporate aircraft unable to depart when planned. Impacts may also become apparent in more spectacular fashion at large-hub airports when closed runways result in extended flight delays, hundreds of cancellations, thousands of stranded passengers, and millions of dollars of lost revenue accompanied by prolonged and often critical media attention. An illuminating example of the value of understanding the history of meteorological events occurred following the December 17–23, 2010 winter storm that severely disrupted London Heathrow Airport (LHR). The disruption from that storm resulted in a formal inquiry that examined LHR’s winter operations practices and recommended changes to avoid a similar disruption. The resulting recommendations affected the updating of the snow plan, coordi- nation among stakeholders, and prioritization of resources, providing for passenger comfort and the organization of the airport’s emergency response structure for dealing with snow emergencies. Following the inquiry, LHR invested £10 million in additional SRE and sub- stantially revised its response to winter storm events. The enquiry noted that the snowfall amount, 7 cm, had an expected recurrence interval of 5 years based on the meteorological period of record at LHR. However the enquiry also noted that a 22-year long stretch of mild weather had created a false sense that the risk of such a storm was remote and could be handled within the existing resources. The following sections describe how characterizing and understanding the range of historical meteorological conditions associated with winter storm events common to an airport’s geogra- phy can help with better planning, communicating, executing winter operations, and managing of expectations. 5.1 Benefits of Utilizing Historical Meteorological Data Most airport operators rely heavily on weather forecasts at the onset of a winter storm event to plan their operations, and on real-time weather observations to adjust their plans as the event unfolds. However, few utilize historical weather data as part of their annual winter operations planning process. Those that do tend to have experienced a significant and highly visible opera- tional disruption due to a winter storm event or are airports that have learned from the experi- ences of other airports. The planning topics identified throughout this guidebook demonstrate C H A P T E R 5

28 A Guidebook for Airport Winter Operations how meteorological data from historical winter storm events, or summaries of statistical analyses of these data, can offer the following benefits: • Aligning performance targets with specific winter event conditions; • Qualifying targeted winter event conditions to facilitate a shared understanding of the invest- ment in infrastructure, equipment, staff, and operating procedures required to achieve per- formance goals; • Facilitating airport operator and stakeholder understanding of the statistical frequency that future winter storm events may exceed the airport’s winter operations capabilities; • Facilitating airport operator and stakeholder risk-based decision making on the acceptability of the airport’s winter operations capabilities and associated operational implications; • Comparing past seasonal performance outcome and efficiency measurements to past winter storm events; and • Comparing recent winter storm events to ranked past events to understand relative magnitude. 5.2 Meteorological Data Sources Meteorological data are widely available for most geo- graphic regions across the United States. Because many airports are designated National Weather Service (NWS) monitoring stations, airport-specific data may be available. The National Oceanic and Atmospheric Administration (NOAA) National Climatic Data Center (NCDC) pro- vides access to digital historical weather and climate data for numerous monitoring stations through its website at: http://www.ncdc.noaa.gov/data-access/quick-links. Of the accessible databases offered by NOAA, the following two databases provide hourly meteorological data: the Inte- grated Surface Database, Hourly, Global (ISD); and the Hourly Precipitation Data Publication/Database. The ISD contains the following primary meteorological parameters that, in conjunction with associated data flags or codes that indicate precipitation type, can be useful in characterizing winter storm events: • Hourly snowfall (indicator of event severity), • Hourly ice accumulation (indicator of event severity), • Hourly liquid accumulation (indicator of snow density in conjunction with snowfall data), • Hourly wind speed (indicator of drifting conditions), • Hourly prevailing wind direction (indicator of pavement surfaces impacted by drifting when used in conjunction with wind speed), • Hourly visibility (indicator of conditions limiting SRE performance), and • Hourly temperature (indicator of snow density). The Hourly Precipitation Data Publication/Database contains only hourly precipitation data available to the hundredth of an inch. The greater measurement accuracy makes this data prefer- able to the precipitation data that may be available through the ISD. These data can also facilitate the calculation of additional parameters of interest (e.g., total event duration). The timespan of available data, or period of record, should be reviewed to determine if there are sufficient data to characterize historical winter storm events. A step-wise process for accessing meteorological data BEST PRACTICE—Weather Observation and Terminology A basic understanding of the fundamentals of weather observation and weather terminology is of benefit to airport operations personnel. Basic meteo rology courses are available at technical schools, colleges, and universities across the coun- try. Many airports train operational personnel as NWS qualified Certified Weather Observers. In fact, the NWS has recently reached out to airports seek- ing to bolster the number of certified observers available at airport locations. The training guide for surface weather observations is available via the following link to the NWS website: http://www. weather.gov/om/forms/resources/SFCTraining.pdf.

Historical Winter Storm Event Data 29 from both databases is available in Appendix A. For statistical calculations of winter storm event frequency, as described later in this chapter, a large dataset of hourly records spanning at least 10 years is required. However, consideration should also be given to changing climatic conditions and the representativeness of meteorological data measurements recorded decades ago. 5.3 Climate Changes Considerations Airports that appear to be experiencing a sustained trending of climatologi- cal change may elect to utilize data from more recent periods (e.g., the past two decades) rather than the full available period of record. While climate change and its effect on airport winter operations are beyond the scope of this guide- book, ACRP Synthesis 33: Airport Climate Adaptation and Resilience reviewed the range of risks to airports from projected climate change and the emerg- ing approaches for handling them. ACRP Project 02-40, “Climate Change Risk Assessment and Adaptation Planning at Airports” (on-going at the time of this writing), is intended to develop a climate change adaptation guidebook for airports that identifies potential impacts from climate change; assesses related airport risks; and provides guidance for managing related uncertainty, devel- oping a prioritized actions plan, and implementing the actions plan as an adap- tive management process (10). 5.4 Data Manipulation and Analysis Once historical meteorological data are downloaded into spreadsheet-type software (e.g., Microsoft Excel®), the dataset must be manipulated to eliminate unwanted data. Data manipulation and analysis can be labor intensive and may require external support, depending upon the availability and capability of air- port personnel. The end goal is to identify and summarize individual winter storm events and associated event parameters documented within the dataset. 5.4.1 Steps for Manipulating Raw Historical Meteorological Data The following steps represent a high-level overview of the steps necessary to manipulate historical data obtained from NOAA and identify individual winter events that can be later ranked: 1. Compile and relate the hourly precipitation data, if obtained from the Hourly Precipitation Data Publication/Database, into the spreadsheet con- taining the ISD data. 2. Maintain one parameter type per column, and all hourly parameter data occurring in the same year, month, day and hour in a single row. 3. Eliminate rows of data for location-specific non-winter season months. 4. Filter the data using accompanying snow or ice-related weather codes to locate and identify the records with the desired codes (data format documentation of the ISD data including a description of data column headers and weather codes is available at: http://hurricane.ncdc.noaa.gov/cdo/3505doc.txt). 5. Identify the beginning and end of storm events within the dataset through the use of the hourly precipitation data (see Table 5-1). Differentiating between events requires defining an inter-event period, or the period of time between the end of one storm event and the beginning of another. This

30 A Guidebook for Airport Winter Operations period should be longer than the intermittent dry periods that may occur during a winter storm event. ACRP Report 81: Winter Design Storm Factor Determination for Airports suggests that geographically driven inter-event periods typically range between 3 and 24 hours (11, p. 4). The closest NWS branch office may be able to assist with determining an appropriate local inter-event period. 6. Utilize the data within the boundaries of each defined storm event to calculate the following parameters by event: – Total event duration (e.g., hours); – Total event snowfall (e.g., inches); – Average hourly snowfall intensity (e.g., total inches/event duration in hours); – Maximum hourly snowfall intensity; – Total event ice accumulation; Table 5-1. Example filtered meteorological data with highlighted winter storm event data. Note: YR = year; MO = month; DA = day; HR = hour; MN = minute; DIR = direction; SPD = speed; GUS = gust; CLG = cloud ceiling, meaning “lowest opaque layer”; SKC = sky condition; VSB = visibility; OVC = overcast; MW = manually observed present weather; TEMP = temperature; DEWP = dew point; ALT = altimeter; and PCP01 = hourly precipitation reported in hundredths of an inch (where “T” means “Trace amount”).

Historical Winter Storm Event Data 31 – Average hourly icing intensity; – Maximum hourly icing intensity; – Total event liquid precipitation accumulation; – Average hourly liquid precipitation intensity; – Average hourly wind speed; – Average hourly prevailing wind direction; – Average hourly visibility; – Minimum hourly visibility; – Average hourly temperature; – Maximum hourly temperature; and – Minimum hourly temperature. 7. Enter the above-identified calculated storm event parameters into a second separate spreadsheet with storm event start date and time as the first two column headers and each additional parameter as a separate column header. There should be only one event per row. 8. Use the second spreadsheet with individual event statistics assembled to conduct a storm event frequency analysis by event parameter. 5.4.2 Event Frequency Analysis A recurrence interval (also referred to as a return period) for a winter storm event parameter is a statistical estimate of the probability that the given event will be equaled or exceeded in any given year. For example, a 100-year recurrence interval winter storm event snowfall total means that there is a 1 in 100 probability or 1-percent chance of the event snowfall occurring or being exceeded in any given year; whereas, a 10-year recurrence interval winter storm event snowfall total means that there is a 1 in 10 probability or 10-percent chance of the event snowfall occur- ring or being exceeded in any given year. Table 5-2 further illustrates the relationship between recurrence interval and probability of occurrence. However, it should be noted that in any given 100-year period, a 100-year recurrence interval event may occur more than once or not at all because weather events are independent of previous weather events. To conduct a frequency analysis and identify event recurrence intervals for the winter storm event parameters summarized in the second spreadsheet described above, 10 or more years of data should be used. More than 10 years of data will increase the confidence level in the analy- sis results. Each storm event, along with all associated data, must be sorted by the parameter of interest in descending order of magnitude. For example, to determine the winter storm event recurrence interval by total event snowfall, sort the data listing the largest total event snowfall Table 5-2. Example recurrence intervals and probabilities of occurrence. Recurrence Interval (years) Probability of Occurrence in Any Given Year Percentage Chance of Occurrence in Any Given Year 100 1 in 100 1% 50 1 in 50 2% 25 1 in 25 4% 10 1 in 10 10% 5 1 in 5 20% 2 1 in 2 50%

32 A Guidebook for Airport Winter Operations value first. This event would receive a ranking of 1; then rank the remaining events by total event snowfall in descending order of magnitude until all of the data are ranked storm event data. Once sorted and ranked, the recurrence interval can by calculated using the following equation: n m Recurrence interval 1 = + where: n is number of years in the data record m is the magnitude ranking As meteorological data from subsequent winter season events are made available by NOAA, event statistics can be calculated and added to the second spreadsheet to determine recurrence intervals. Maintenance of these spreadsheets on a minimum annual frequency will require sub- stantially less effort than the initial effort to analyze periods of record data. Once the recurrence intervals for the winter storm events summarized in the second spread- sheet have been calculated, the information can offer perspective on past winter operations per- formance. For example, if an airport operator and its stakeholders understand that significant flight delays occur due to snow removal operations associated with a 2-year recurrence interval total event snowfall, they can plan around the 50-percent chance that this level of operational disruption may occur during every winter season. If the likelihood of this disruption is too high, new performance goals and objectives to reduce the likelihood of flight delays can be set and the airport operator can increase its ability to manage greater recurrence interval winter storm events. This is the concept upon which identifying threshold winter-event conditions and setting performance targets is based. Identifying threshold winter-event conditions and setting performance targets are discussed in Chapter 7.

Next: Chapter 6 - Winter Operations Performance Measurement »
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TRB’s Airport Cooperative Research Program (ACRP) Report 123: A Guidebook for Airport Winter Operations provides direction to airport facilities as they prepare for, operate during, and recover from disruptive winter events. The report also provides tips for managing the overall passenger experience and provides guidance on the levels of investment needed to implement an effective winter operations program.

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