No abstract

Recommendations by the National Research Council (NRC), the National Institute of Standards and Technology (NIST), and Weather-Ready Nation workshop participants have encouraged the National Oceanic and Atmospheric Administration (NOAA) and the broader weather enterprise to explore and expand the use of probabilistic information to convey weather forecast uncertainty. Forecasting a Continuum of Environmental Threats (FACETs) is a concept being explored by NOAA to address those recommendations and also potentially shift the National Weather Service (NWS) from (primarily) teletype-era, deterministic watch–warning products to high-resolution, probabilistic hazard information (PHI) spanning periods from days (and longer) to within minutes of high-impact weather and water events. FACETs simultaneously i) considers a reinvention of the NWS hazard forecasting and communication paradigm so as to deliver multiscale, user-specific probabilistic guidance from numerical weather prediction ensembles and ii) provides a comprehensive framework to organize the physical, social, and behavioral sciences, the technology, and the practices needed to achieve that reinvention. The first applications of FACETs have focused on thunderstorm phenomena, but the FACETs concept is envisioned to extend to the attributes of any environmental hazards that can be described probabilistically (e.g., winter, tropical, and aviation weather). This paper introduces the FACETs vision, the motivation for its creation, the research and development under way to explore that vision, its relevance to operational forecasting and society, and possible strategies for implementation.

The role of the human forecaster in improving upon the accuracy of numerical weather prediction is explored using multiyear verification of human-generated short-range precipitation forecasts and mediumrange maximum temperature forecasts from the Weather Prediction Center (WPC). Results show that human-generated forecasts improve over raw deterministic model guidance. Over the past two decades, WPC human forecasters achieved a 20%-40% improvement over the North American Mesoscale (NAM) model and the Global Forecast System (GFS) for the 1 in. (25.4 mm) (24 h) 21 threshold for day 1 precipitation forecasts, with a smaller, but statistically significant, 5%-15% improvement over the deterministic ECMWF model. Medium-range maximum temperature forecasts also exhibit statistically significant improvement over GFS model output statistics (MOS), and the improvement has been increasing over the past 5 yr. The quality added by humans for forecasts of high-impact events varies by element and forecast projection, with generally large improvements when the forecaster makes changes $88F (4.48C) to MOS temperatures. Human improvement over guidance for extreme rainfall events [3 in. (76.2 mm) (24 h) 21 ] is largest in the short-range forecast. However, human-generated forecasts failed to outperform the most skillful downscaled, biascorrected ensemble guidance for precipitation and maximum temperature available near the same time as the human-modified forecasts. Thus, as additional downscaled and bias-corrected sensible weather element guidance becomes operationally available, and with the support of near-real-time verification, forecaster training, and tools to guide forecaster interventions, a key test is whether forecasters can learn to make statistically significant improvements over the most skillful of this guidance. Such a test can inform to what degree, and just how quickly, the role of the forecaster changes.

A climatological and composite study of banded precipitation in the northeast United States during the cold season (October–April) is presented. Precipitation systems in the northeast United States in April 1995 and from October 1996 to April 2001 that exhibited greater than 25.4 mm (1 in.) of rainfall, or 12.7 mm (0.5 in.) liquid equivalent, were identified as cases for study. A total of 111 cases were identified during this period, of which 88 had available radar data. Of these cases, 75 exhibited banded structure whereas 13 did not. A band classification scheme was developed from a subset of study cases. Application of the classification scheme to the 88 cases revealed that banded cases can exhibit a variety of banded events during their evolution. Single-banded events were the most common (48), followed by transitory (40), narrow cold frontal (36), multi (29), and undefined (9). Further investigation of the single-banded events highlighted banded structure in the comma-head portion of storms, with 81% of these events exhibiting a majority of their length in the northwest quadrant of the surface cyclone. Composites were calculated for cases exhibiting single-banded events in the northwest quadrant of the surface cyclone and for nonbanded cases to distinguish synoptic and mesoscale flow regimes associated with banded events and nonbanded cases. The banded composite was marked by cyclogenesis and the development of a closed midlevel circulation. This flow configuration was associated with deformation and strong midlevel frontogenesis northwest of the surface cyclone center, which coincided with the mean band position. The nonbanded composite exhibited a much weaker cyclone located in the confluent entrance region of an upper-level jet. The absence of a closed midlevel circulation in the nonbanded composite limited deformation and associated frontogenesis northwest of the surface cyclone. Cross-section analysis through the respective composite frontogenesis maxima showed that the banded composite frontal zone exhibited stronger and deeper frontogenesis and weaker conditional stability than the nonbanded composite frontal zone. Case studies from the northeast United States confirm the composite results, highlighting the importance of deep-layer frontogenesis coincident with weak conditional stability. These results are in qualitative agreement with the Sawyer–Eliassen equation, which predicts that the frontogenetical response will be enhanced (reduced) in the presence of small (large) moist symmetric stability.

Extreme quantitative precipitation forecast (QPF) performance is baselined and analyzed by NOAA's Hydrometeorology Testbed (HMT) using 11 yr of 32-km gridded QPFs from NCEP's Weather Prediction Center (WPC). The analysis uses regional extreme precipitation thresholds, quantitatively defined as the 99th and 99.9th percentile precipitation values of all wet-site days from 2001 to 2011 for each River Forecast Center (RFC) region, to evaluate QPF performance at multiple lead times. Five verification metrics are used: probability of detection (POD), false alarm ratio (FAR), critical success index (CSI), frequency bias, and conditional mean absolute error (MAE cond ). Results indicate that extreme QPFs have incrementally improved in forecast accuracy over the 11-yr period. Seasonal extreme QPFs show the highest skill during winter and the lowest skill during summer, although an increase in QPF skill is observed during September, most likely due to landfalling tropical systems. Seasonal extreme QPF skill decreases with increased lead time. Extreme QPF skill is higher over the western and northeastern RFCs and is lower over the central and southeastern RFC regions, likely due to the preponderance of convective events in the central and southeastern regions. This study extends the NOAA HMT study of regional extreme QPF performance in the western United States to include the contiguous United States and applies the regional assessment recommended therein. The method and framework applied here are readily applied to any gridded QPF dataset to define and verify extreme precipitation events. * Current affiliation: