Forecast-Based Decision Support for San Francisco International Airport: A NextGen Prototype System That Improves Operations during Summer Stratus Season

Abstract

During summer, marine stratus encroaches into the approach to San Francisco International Airport (SFO) bringing low ceilings. Low ceilings restrict landings and result in a high number of arrival delays, thus impacting the National Air Space (NAS). These delays are managed by implementation of ground delay programs (GDPs), which hold traffic on the ground at origination airports in anticipation of insufficient arrival capacity at SFO. In an effort to reduce delays and improve both airport and NAS efficiency, the Federal Aviation Administration (FAA) funded a research effort begun in 1995 to develop an objective decision support system to aid forecasters in the prediction of stratus clearing times. By improving forecasts at this major airport, the scope and duration of ground and airborne holds can be reduced. The Marine Stratus Forecast System (MSFS) issues forecasts both deterministically and probabilistically. Following transition to NWS operations in 2004, the system continued to provide reliable forecasts but showed no significant improvement in delay reduction. Changes to the FAA GDP issuance procedures in 2008 allowed them to utilize the improved forecasts, leading to quantifiable reductions in ground and airborne holds for SFO equating to dollars saved. To further reduce delays, a refined statistically based model, the Ground Delay Parameters Selection Model (GPSM) for selecting an optimal ground delay strategy has been developed, utilizing the available archive of objective MSFS probabilistic forecasts and accompanying traffic flow data. This effort represents one of the first systematic attempts to integrate objective probabilistic weather information into the air traffic flow decision process, which is a cornerstone element of the FAA's visionary NextGen program.

On the Parameterization of Convective Downdrafts for Marine Stratocumulus Clouds

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Cloud height measurement by a network of all-sky-imagers

“…CBH is used to validate and improve climate models (Costa-Surós et al, 2013) and numeric weather prediction models (Hogan et al, 2009). In aviation, CBH is important to air traffic controllers (Khlopenkov et al, 2019;Reynolds et al, 2012;Isaac et al, 2014). As clouds are the major cause of variability of the solar resource, they are of special interest for solar power applications.…”

On the use of Bayesian decision theory for issuing natural hazard warnings

“…Reynolds et al [23] describes a decision support tool that uses probabilistic forecasts of cloud layer to minimize flight delays at the San Francisco airport. Different response scenarios were considered and the concept of a loss function was introduced, in order to select the scenario that minimizes expected loss.…”