NAS WITI vs. Combined WITI-FA and Delta (“forecast goodness”) Negative delta = Under-forecast of weather/traffic impact Positive delta = Over-forecast of weather/traffic impact 30-day period ending 08/29/2012 WITI = 100 is a “normally- impacted ” day Delta > +/-50 may indicate a forecast “issue”

2 En-route and Terminal WITI-FA 30-Day Period Ending 08/29/2012 En route Convection E-WITI (NCWD) vs. E-WITI-FA (CCFP) Terminal Weather T-WITI (METARs) vs. T-WITI-FA (TAFs) 6/25/2016 Positive “Delta”: Over-forecast Negative “Delta”: Under-forecast Positive “Delta”: Over-forecast Negative “Delta”: Under-forecast

6/25/20163 WITI Breakdown by Component Last 7 Days, Ending 08/29/2012 Delay data for the last 2 days of any 7-day period is preliminary

TWITI, 7-Day Period Ending 08/29/2012, by NWS Region TWITI shows potential operational impact of IMC, Wind, Winter precipitation, and Local convective Wx Positive Deltas (bars): over-forecast 4 Negative Deltas (bars): under-forecast Terminal Weather T-WITI (METARs) vs. T-WITI-FA (TAFs)

5 AvMet Applications’ Website For more detailed drill-down and analysis, please go to 6/25/2016

Back-Up Section

WITI / WITI-FA Components WITI model consists of two principal components: En-route (E-WITI) and Terminal (T- WITI). En-route WITI (E-WITI) reflects the impact of en-route convective weather and en- route traffic demand (‘flows’ between OEP34 airports) on the NAS. Terminal WITI (T-WITI) reflects the impact of local airport weather and local traffic demand on the airport’s operation: Airport capacity can decrease due to inclement weather (low ceilings, rain, snow, wind etc). Arrival and departure rates may be reduced, resulting in delays and/or cancellations. If scheduled traffic demand exceeds airport capacity (be it in good or bad weather), queuing delays ensue. These delays can quickly grow exponential; in some cases, wide-spread cancellations are the only way to limit non-linear growth of delays. T-WITI reflects both the linear increase in delays (some impact of inclement weather but airport’s capacity remains higher than traffic demand) and, in more severe cases, non-linear increase in delays (impact of weather and/or traffic demand grows exponentially when demand exceeds airport’s capacity) WITI is computed using actual (recorded) weather. WITI-FA (“Forecast Accuracy”) is computed using forecast weather, both en-route convective and terminal.

WITI Breakdown by Cause Explanation to Slide 3 WITI software can distinguish the following factors: En-route convective weather. This shows convective weather impact on an airport’s inbound/outbound flows within approx. 500-NM range. This component does not affect queuing delay at the airport. Local convective weather. This reflects how convective weather in the vicinity (<= 100 NM) or directly over the airport reduces airport’s capacity. It may affect queuing delay. Wind. Any time there is a wind greater than 20 Kt, or there is precipitation and wind greater than 15 Kt, the corresponding impact is recorded. Airport capacity may decrease, i.e. queuing delays may increase. Winter Precip (snow, freezing rain, ice etc). The corresponding impact is recorded. Airport capacity may decrease, i.e. queuing delays may increase. IMC. Ceiling or visibility below airport specific minima; fog; and heavy rain. The corresponding FAA capacity benchmarks for IMC are used. Queuing delays may increase. Queuing Delay (No Weather) plus Ripple Effects. No particular weather factor recorded locally for the given airport / given hour but WITI software computed that there would be queuing delays. This can be simply due to high traffic demand or in an aftermath of a major weather event when queuing delays linger on (even as the weather has moved out). Additionally, Ripple Effects are recorded in this component. For example, if ORD experiences departure queuing delays, its corresponding destination airports will get some additional arrival queuing delay. Other. Includes minor impacts due to light/moderate rain or drizzle but ceilings/visibility above VFR minima; also unfavorable RWY configuration usually due to light-to-moderate winds (15-20 Kt or even 10 Kt) that prevent optimum-capacity runway configurations from being used. “Convective” “Non- convective” “Other”

Rolling 4-hr Look-ahead Forecast “4-hr TAF” is mentioned throughout this slide set. In actuality, Terminal Area Forecasts (TAFs) are issued every 6 hours, with amendments issued at irregular time intervals if/as necessary. From this TAF “stream”, the WITI software constructs a rolling 4-hr look-ahead forecast. If, for instance, it is 1300Z and an operator at airport NNN would like to know the expected weather situation at 1700Z, what is the TAF information available to him/her at 1300Z? It could be the standard 1200Z TAF valid through 1800Z) with perhaps an amendment issued at 1300Z. An hour later, at 1400Z, if the operator needs to know the forecast for 1800Z, he or she might still have the same information as at 1300Z but perhaps a new amendment has been issued, and so on. Rolling 2-hr, 4-hr and 6-hr CCFP (convective forecast) is interpreted in a similar fashion. There are no amendments as in TAF. CCFP is issued every 2 hours at odd hours (1300Z, 1500Z, …) as a set of three forecasts. A CCFP forecast for even hours is an interpolation of these 2-hr CCFPs.

Arrival Rate Charts (Analysis) Things to keep in mind: WITI model estimated rates show potential airport capacity given the perceived or expected weather impact. Direct comparison between WITI model-estimated and actual arrival rates should be made with caution: the WITI model does not reflect all the factors, events and human decisions that are behind a specific actual arrival rate. Comparison with facility-called rates can help to understand these effects. Recorded (actual or forecast) weather data is discrete: for example, wind is recorded in hourly intervals and its direction can vary, affecting what WITI model selects as the optimal runway configuration. Or, snow can start and stop. But actual impact of weather can be longer-lasting (e.g. snow removal) and an airport cannot react to wind changes by changing runway configuration in an abrupt manner. The result may be a larger variability in WITI model-forecast rates vs. actual arrival rates. Non-weather factors, as well as weather in other parts of the NAS, may impact airport capacity on a particular day; this is not reflected in WITI model-based arrival rates (they are based only on local weather). Suggested uses for the arrival-rates charts: Significant differences between METAR- and TAF-based arrival rates may be an indication of an over- or under-forecast of terminal weather. When actual arrival rates may be noticeably lower than scheduled, this may in some instances indicate an impact of an inaccurate weather forecast. Scheduled and actual arrival rates (solid purple and dashed blue lines on the chart) are extracted directly from ASPM data. METAR and Rolling-4hr-lookahead-TAF based rates (red and yellow lines) are WITI model estimates based on historical data and FAA airport capacity benchmarks. “GDP rate” (light blue line) is either a program rate or, if no GDP was in place, maximum VMC arrival rate for the airport.

Snow/Ice Impact Quantification Moderate snow/ice may in some instances cause higher impact on airports (delays) than indicated by WITI. The reason is that even as the snowfall stops and winter weather moves out, snow and ice removal may take a long time; this is not reflected in METAR/TAF data and hence the WITI may be lower. Also, it takes time for airlines to restore their schedules back to normal, which again leads to higher delays compared to perceived weather impact. Conversely, on days with very heavy impact of winter weather, WITI can be much higher than the normalized Delay. This is due to massive cancellations that lower traffic demand. However, in these cases WITI correctly reflects the overall weather impact on the NAS. Typically, on the next day, when the winter weather moves out, NAS Delay metric is significantly higher than WITI (as airlines work to restore schedules back to normal).