Evaluation of Convective Forecast Error Variability by Operationally Relevant Meteorological Characteristics

Adverse weather remains the most disruptive constraint in the National Airspace System (NAS), contributing to the vast majority of air traffic delay, cancellation, and diversion impacts that occur annually. This disruption and the need to effectively predict the occurrence of adverse weather has motivated the development of multiple weather forecast systems and products which, due to the highly non-linear nature of the atmosphere, have associated forecast errors. To date, operational use of these forecasts in making risk-managed, air traffic impact mitigation decisions is often hampered by the lack of explicit, objective awareness of the weather forecast errors. Moreover, the availability of multiple deterministic and probabilistic forecast products, without accompanying, objective information of how their collective use may increase confidence in the forecast through improved characterization and depiction of individual forecast errors, limits the applicability of multiple data sources to operational decision-making. These shortfalls erode abilities to define, coordinate, and execute effective Air Traffic Management (ATM) strategies, resulting in inconsistent solutions which fail to limit avoidable impacts.

Currently, for purposes specific to air traffic operations, there is a dearth of objective data and information on forecast error variability across different weather characteristics such as time of day, location, atmospheric regime, and constraint type. Understanding this variability can improve estimates of weather event errors through intelligent combination of errors across different types of forecast models (i.e., probabilistic and deterministic). Decision-makers can also often struggle with making deterministic decisions based on probabilistic information and could benefit from the identification of significant probability thresholds (and associated expectations for forecast performance) in specific meteorological (or ATM impact) scenarios. Error characterization with a component of translating probabilistic forecasts into thresholds for actionable ATM guidance may assist with some of these decision-making challenges.

Focusing first on convective weather, this paper assesses forecast error variability by quantifying and evaluating forecast errors for several pertinent convective weather forecast systems in three relevant ATM planning time horizons. Methods are presented for quantifying several ATM-related error types for two probabilistic and one deterministic forecast. These errors are evaluated across a suite of historical events comprised of various combinations of weather characteristics significant to ATM - time of day, convective weather pattern, and region of the CONUS. Forecast error variability is discussed, stratified by these convective characteristics, ATM planning period, and error type for both probabilistic and deterministic forecasts to facilitate potential combinations of these forecasts to more robustly capture and depict forecast error. Identification of critical probability thresholds for events with each combination of convective characteristics within each planning horizon will also be presented for both probabilistic forecast systems along with its potential utility in ATM decision-making. Opportunities to ensemble multi-forecast error characteristics to improve forecast uncertainty guidance applicable for ATM decision-making will be presented and extensions of this overall approach and convective weather error analysis results to other types of aviation weather will be discussed.