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Abstract
As part of an investigation into terminal airspace productivity sponsored by the NASA Ames Research Center, a study was performed at the Forecast Systems Laboratory to investigate sources of wind forecast error and to assess differences in wind forecast accuracy between the 60-km Rapid Update Cycle, version 1 (RUC-1), and the newer 40-km RUC-2. Improved knowledge of these errors is important for development of air traffic management automation tools under development at NASA Ames and elsewhere. This information is also useful for operational users of RUC forecast winds. To perform this study, commercial aircraft reports of wind reported through Aircraft Communications, Addressing, and Reporting System (ACARS) were collected in a region over the western and central United States for a 13-month period, along with RUC-1 and RUC-2 wind forecasts. Differences between forecasts and ACARS observations and estimates of ACARS wind observation error itself were both calculated.
It was found that rms vector differences between observations and forecasts from either version of the RUC increased as wind speed increased, and also as altitude increased and in winter months (both associated with higher wind speed). Wind errors increased when thunderstorms were nearby and were smaller in wintertime precipitation situations. The study also showed that considerable progress has been made in the accuracy of wind forecasts to be used for air traffic management by the introduction of the RUC-2 system, replacing the previous RUC-1 system. Improvement was made both in the intrinsic accuracy as well as in the time availability, both contributing to the overall improvement in the actual wind forecast available for air traffic management purposes. Using 3-h forecasts, RUC-2 demonstrated a reduction in mean daily rms vectors of approximately 10% over that for RUC-1 based on accuracy improvements alone. This error reduction increased to about 22% when time availability improvements were added. It was also found that the degree of improvement from the RUC-2 increased substantially for periods with a large number of significant wind errors. The percentage of individual vector errors greater than 10 m s−1 was reduced by RUC-2 from 8% (RUC-1) to 3% overall and from 17% to 7% during the worst month. Such peak error periods have a strong impact on air traffic management automation tools. Last, it was found that the estimated trajectory projection errors from the RUC-2 using 1–2-h forecasts averaged 9 s for ascent/descent flight segments of approximately 15 min, and about 10 s for en route segments of the same duration.
Abstract
As part of an investigation into terminal airspace productivity sponsored by the NASA Ames Research Center, a study was performed at the Forecast Systems Laboratory to investigate sources of wind forecast error and to assess differences in wind forecast accuracy between the 60-km Rapid Update Cycle, version 1 (RUC-1), and the newer 40-km RUC-2. Improved knowledge of these errors is important for development of air traffic management automation tools under development at NASA Ames and elsewhere. This information is also useful for operational users of RUC forecast winds. To perform this study, commercial aircraft reports of wind reported through Aircraft Communications, Addressing, and Reporting System (ACARS) were collected in a region over the western and central United States for a 13-month period, along with RUC-1 and RUC-2 wind forecasts. Differences between forecasts and ACARS observations and estimates of ACARS wind observation error itself were both calculated.
It was found that rms vector differences between observations and forecasts from either version of the RUC increased as wind speed increased, and also as altitude increased and in winter months (both associated with higher wind speed). Wind errors increased when thunderstorms were nearby and were smaller in wintertime precipitation situations. The study also showed that considerable progress has been made in the accuracy of wind forecasts to be used for air traffic management by the introduction of the RUC-2 system, replacing the previous RUC-1 system. Improvement was made both in the intrinsic accuracy as well as in the time availability, both contributing to the overall improvement in the actual wind forecast available for air traffic management purposes. Using 3-h forecasts, RUC-2 demonstrated a reduction in mean daily rms vectors of approximately 10% over that for RUC-1 based on accuracy improvements alone. This error reduction increased to about 22% when time availability improvements were added. It was also found that the degree of improvement from the RUC-2 increased substantially for periods with a large number of significant wind errors. The percentage of individual vector errors greater than 10 m s−1 was reduced by RUC-2 from 8% (RUC-1) to 3% overall and from 17% to 7% during the worst month. Such peak error periods have a strong impact on air traffic management automation tools. Last, it was found that the estimated trajectory projection errors from the RUC-2 using 1–2-h forecasts averaged 9 s for ascent/descent flight segments of approximately 15 min, and about 10 s for en route segments of the same duration.
Abstract
Previous work has considered tornado occurrence with respect to radar data, both WSR-88D and mobile research radars, and a few studies have examined techniques to potentially improve tornado warning performance. To date, though, there has been little work focusing on systematic, large-sample evaluation of National Weather Service (NWS) tornado warnings with respect to radar-observable quantities and the near-storm environment. In this work, three full years (2016–18) of NWS tornado warnings across the contiguous United States were examined, in conjunction with supporting data in the few minutes preceding warning issuance, or tornado formation in the case of missed events. The investigation herein examines WSR-88D and Storm Prediction Center (SPC) mesoanalysis data associated with these tornado warnings with comparisons made to the current Warning Decision Training Division (WDTD) guidance. Combining low-level rotational velocity and the significant tornado parameter (STP), as used in prior work, shows promise as a means to estimate tornado warning performance, as well as relative changes in performance as criteria thresholds vary. For example, low-level rotational velocity peaking in excess of 30 kt (15 m s−1), in a near-storm environment, which is not prohibitive for tornadoes (STP > 0), results in an increased probability of detection and reduced false alarms compared to observed NWS tornado warning metrics. Tornado warning false alarms can also be reduced through limiting warnings with weak (<30 kt), broad (>1 n mi; 1 n mi = 1.852 km) circulations in a poor (STP = 0) environment, careful elimination of velocity data artifacts like sidelobe contamination, and through greater scrutiny of human-based tornado reports in otherwise questionable scenarios.
Abstract
Previous work has considered tornado occurrence with respect to radar data, both WSR-88D and mobile research radars, and a few studies have examined techniques to potentially improve tornado warning performance. To date, though, there has been little work focusing on systematic, large-sample evaluation of National Weather Service (NWS) tornado warnings with respect to radar-observable quantities and the near-storm environment. In this work, three full years (2016–18) of NWS tornado warnings across the contiguous United States were examined, in conjunction with supporting data in the few minutes preceding warning issuance, or tornado formation in the case of missed events. The investigation herein examines WSR-88D and Storm Prediction Center (SPC) mesoanalysis data associated with these tornado warnings with comparisons made to the current Warning Decision Training Division (WDTD) guidance. Combining low-level rotational velocity and the significant tornado parameter (STP), as used in prior work, shows promise as a means to estimate tornado warning performance, as well as relative changes in performance as criteria thresholds vary. For example, low-level rotational velocity peaking in excess of 30 kt (15 m s−1), in a near-storm environment, which is not prohibitive for tornadoes (STP > 0), results in an increased probability of detection and reduced false alarms compared to observed NWS tornado warning metrics. Tornado warning false alarms can also be reduced through limiting warnings with weak (<30 kt), broad (>1 n mi; 1 n mi = 1.852 km) circulations in a poor (STP = 0) environment, careful elimination of velocity data artifacts like sidelobe contamination, and through greater scrutiny of human-based tornado reports in otherwise questionable scenarios.
