Abstract

The forecast uncertainty of mesoscale snowband formation and evolution is compared using predictions from a 16-member multimodel ensemble at 12-km grid spacing for the 25 December 2002, 12 February 2006, and 14 February 2007 northeast U.S. snowstorms. Using these predictions, the case-to-case variability in the predictability of band formation and evolution is demonstrated. Feature-based uncertainty information is also presented as an example of what may be operationally feasible from postprocessing information from future short-range ensemble forecast systems. Additionally, the initial condition sensitivity of band location in each case is explored by contrasting the forecast evolutions of initial condition members with large differences in snowband positions. Considerable uncertainty in the occurrence, and especially timing and location, of band formation and subsequent evolution was found, even at forecast projections <24 h. The ensemble provided quantitative mesoscale band uncertainty information, and differentiated between high-predictability (14 February 2007) and low-predictability (12 February 2006) cases. Among the three cases, large (small) initial differences in the upper-level PV distribution and surface mean sea level pressure of the incipient cyclone were associated with large (small) differences in forecast snowband locations, suggesting that case-to-case differences in predictability may be related to the quality of the initial conditions. The complexity of the initial flow may also be a discriminator. Error growth was evident in each case, consistent with previous mesoscale predictability research, but predictability differences were not correlated to the degree of convection. Discussion of these results and future extensions of the work are presented.

Abstract

Key results of a comprehensive survey of U.S. National Weather Service operational forecast managers concerning the assessment and communication of forecast uncertainty are presented and discussed. The survey results revealed that forecasters are using uncertainty guidance to assess uncertainty, but that limited data access and ensemble underdispersion and biases are barriers to more effective use. Some respondents expressed skepticism as to the added value of formal ensemble guidance relative to simpler approaches of estimating uncertainty, and related the desire for feature-specific ensemble verification to address this skepticism. Respondents reported receiving requests for uncertainty information primarily from sophisticated users such as emergency managers, and most often during high-impact events. The largest request for additional training material called for simulator-based case studies that demonstrate how uncertainty information should be interpreted and communicated.

Respondents were in consensus that forecasters should be significantly involved in the communication of uncertainty forecasts; however, there was disagreement regarding if and how forecasters should adjust objective ensemble guidance. It is contended that whether forecasters directly modify objective ensemble guidance will ultimately depend on how the weather enterprise views ensemble output (as the final forecast or as a guidance supporting conceptual understanding), the enterprise’s commitment to provide the necessary supporting forecast infrastructure, and how rapidly ensemble weaknesses such as underdispersion, biases, and resolution are addressed.

The survey results illustrate that forecasters’ operational uncertainty needs are intimately tied to the end products and services they produce. Thus, it is critical that the process to develop uncertainty information in existing or new products or services be a sustained collaborative effort between ensemble developers, forecasters, academic partners, and users. As the weather enterprise strives to provide uncertainty information to users, it is asserted that addressing the forecaster needs identified in this survey will be a prerequisite to achieve this goal.

Abstract

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.

Abstract

An ingredients-based, time- and scale-dependent forecast strategy for anticipating cold season mesoscale band formation within eastern U.S. cyclones is presented. This strategy draws on emerging conceptual models of mesoscale band development, advances in numerical weather prediction, and modern observational tools. As previous research has shown, mesoscale band development is associated with frontogenesis in the presence of weak moist symmetric stability and sufficient moisture. These three parameters—frontogenesis, weak moist symmetric stability, and moisture—are used as the ingredients for identifying mesoscale band development in this strategy. At forecast projections beyond 2 days, the strategy assesses whether cyclogenesis is expected. Within 2 days of the event, the strategy places the band ingredients in the context of the broader synoptic flow, with attention to where deformation zones are present, to assess whether banding is possible. Within 1 day of the event, the strategy focuses on assessment of the ingredients to outline when and where band formation is favored. Plan-view and cross-sectional analyses of gridded model fields in conjunction with high-resolution model guidance are used to assess the likelihood of banding and to outline the threat area. Within 12 h, short-range forecasts of the band ingredients are evaluated in concert with observations to make specific band predictions. Particular emphasis is placed on the evolution of the frontogenetic forcing and moist symmetric stability. During the event, trends in observations and short-range model forecasts are used to anticipate the movement, intensity, and dissipation of the band. The benefits and practical challenges associated with the proposed strategy are illustrated through its operational application to the 25 December 2002 northeast U.S. snowstorm, during which intense mesoscale snowband formation occurred. Forecast products from this event demonstrate how the forecast strategy can lead to heightened situational awareness, in this case resulting in accurate band forecasts. This application shows that accurate operational forecasts of mesoscale bands can be made based on our current conceptual understanding, observational tools, and modeling capabilities.

Abstract

This paper is the first of two papers that examines the organization of the precipitation field in central U.S. cold-season cyclones involving inverted troughs. The first portion of the study examines the varying precipitation distribution that occurred during a 6-yr synoptic climatology of inverted trough cases. The definition of inverted trough cases has been expanded from the groundbreaking work by Keshishian et al. by 1) not requiring a closed cyclonic isobar along the frontal wave along the conventional surface front and 2) not requiring a surface thermal gradient to be present along the inverted trough. Only 8.5% of the expanded dataset produced the precipitation primarily occurring to the west of the inverted trough (“behind” cases) as seen in Keshishian et al. The largest group of cases, comprising about 40% of the cases, produced precipitation that primarily occurred between the inverted trough and the conventional warm front (“ahead” cases). A composite study compared a subset of the ahead cases with a subset of the behind cases. The ahead cases tended to be more progressive with a stronger jet stream located over the center of the parent low. Broad warm-air advection and frontogenesis in the lower troposphere were observed between the inverted trough and the surface warm front. Cold-air advection to the west of the inverted trough precluded the development of “wraparound precipitation.” In contrast, the behind cases had a stronger low-latitude wave couplet with a trough upstream of the surface low and a ridge downstream. The region of warm-air advection and frontogenesis were displaced to the west of the inverted trough and surface cyclone. In addition, the entrance region of a southwest–northeast-oriented jet streak aided the development of ascent to the west of the inverted trough while precluding the development of precipitation to the north of the conventional warm front. Thus, the inverted trough tended to act like a warm front in behind cases, as shown by Keshishian et al. Composites were also computed at both 12 and 24 h before inverted trough formation in order to generate comparisons useful to operational applications. Case study results for both ahead and behind cases will be compared with the composite cases in the companion paper.

Abstract

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 medium-range 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)⁻¹ 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 ≥8°F (4.4°C) to MOS temperatures. Human improvement over guidance for extreme rainfall events [3 in. (76.2 mm) (24 h)⁻¹] is largest in the short-range forecast. However, human-generated forecasts failed to outperform the most skillful downscaled, bias-corrected 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.

Abstract

U.S. National Weather Service (NWS) forecasters assess and communicate hazardous weather risks, including the likelihood of a threat and its impacts. Convection-allowing model (CAM) ensembles offer potential to aid forecasting by depicting atmospheric outcomes, including associated uncertainties, at the refined space and time scales at which hazardous weather often occurs. Little is known, however, about what CAM ensemble information is needed to inform forecasting decisions. To address this knowledge gap, participant observations and semistructured interviews were conducted with NWS forecasters from national centers and local weather forecast offices. Data were collected about forecasters’ roles and their forecasting processes, uses of model guidance and verification information, interpretations of prototype CAM ensemble products, and needs for information from CAM ensembles. Results revealed forecasters’ needs for specific types of CAM ensemble guidance, including a product that combines deterministic and probabilistic output from the ensemble as well as a product that provides map-based guidance about timing of hazardous weather threats. Forecasters also expressed a general need for guidance to help them provide impact-based decision support services. Finally, forecasters conveyed needs for objective model verification information to augment their subjective assessments and for training about using CAM ensemble guidance for operational forecasting. The research was conducted as part of an interdisciplinary research effort that integrated elicitation of forecasters’ CAM ensemble needs with model development efforts, with the aim of illustrating a robust approach for creating information for forecasters that is truly useful and usable.