ABSTRACT
An object-based verification methodology for the NSSL Experimental Warn-on-Forecast System for ensembles (NEWS-e) has been developed and applied to 32 cases between December 2015 and June 2017. NEWS-e forecast objects of composite reflectivity and 30-minute rotation tracks of updraft helicity are matched to corresponding objects in Multi-Radar Multi-Sensor data on space and time scales typical of a National Weather Service warning. Object matching allows contingency table-based verification statistics to be used to establish baseline performance metrics for NEWS-e thunderstorm and mesocyclone forecasts. NEWS-e critical Success Index (CSI) scores of reflectivity (updraft helicity) forecasts decrease from approximately 0.7 (0.4) to 0.4 (0.2) over 3 hours of forecast time. CSI scores decrease through the forecast period, indicating that errors have not saturated and skill is retained at 3 hours of forecast time. Lower verification scores for rotation track forecasts are primarily a result of a high frequency bias. Comparison of different system configurations used in 2016 and 2017 show an increase in skill for 2017 reflectivity forecasts, attributable mainly to improvements in the forecast initial condition. A small decrease in skill in 2017 rotation track forecasts is likely a result of sample differences between 2016 and 2017. Although large case-to-case variation is present, evidence is found that NEWS-e forecast skill improves with increasing object size and intensity, as well as in mesoscale environments in which an enhanced or higher risk of severe thunderstorms was forecast.
ABSTRACT
Recent studies have found that flight through deep convective storms and ingestion of high mass concentrations of ice crystals, also known as high ice water content (HIWC), into aircraft engines can adversely impact aircraft engine performance. These aircraft engine icing events caused by HIWC have been documented during flight in weak reflectivity regions near convective updraft regions that do not appear threatening in onboard weather radar data. Three airborne field campaigns were conducted in 2014 and 2015 to better understand how HIWC is distributed in deep convection, both as a function of altitude and proximity to convective updraft regions, and to facilitate development of new methods for detecting HIWC conditions, in addition to many other research and regulatory goals. This paper describes a prototype method for detecting HIWC conditions using geostationary (GEO) satellite imager data coupled with in-situ total water content (TWC) observations collected during the flight campaigns. Three satellite-derived parameters were determined to be most useful for determining HIWC probability: 1) the horizontal proximity of the aircraft to the nearest overshooting convective updraft or textured anvil cloud, 2) tropopause-relative infrared brightness temperature, and 3) daytime-only cloud optical depth. Statistical fits between collocated TWC and GEO satellite parameters were used to determine the membership functions for the fuzzy logic derivation of HIWC probability. The products were demonstrated using data from several campaign flights and validated using a subset of the satellite-aircraft collocation database. The daytime HIWC probability was found to agree quite well with TWC time trends and identified extreme TWC events with high probability. Discrimination of HIWC was more challenging at night with IR-only information. The products show the greatest capability for discriminating TWC ≥ 0.5 g m-3. Product validation remains challenging due to vertical TWC uncertainties and the typically coarse spatio-temporal resolution of the GEO data.
