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Steven G. Decker

Calls for moving from a deterministic to a probabilistic view of weather forecasting have become increasingly urgent over recent decades, yet the primary national forecasting competition and many in-class forecasting games are wholly deterministic in nature. To counter these conflicting trends, a long-running forecasting game at Rutgers University has recently been modified to become probabilistic in nature. Students forecast high- and low-temperature intervals and probabilities of precipitation for two locations: one fixed at the Rutgers cooperative observing station, the other chosen for each forecast window to maximize difficulty. Precipitation errors are tabulated with a Brier score, while temperature errors contain a sharpness component dependent on the width of the forecast interval and an interval miss component dependent on the degree to which the verification falls within the interval.

The inclusion of a probabilistic forecasting game allows for the creation of a substantial database of forecasts that can be analyzed using standard probabilistic approaches, such as reliability diagrams, relative operating characteristic curves, and histograms. Discussions of probabilistic forecast quality can be quite abstract for undergraduate students, but the use of a forecast database that students themselves help construct motivates these discussions and helps students make connections between their forecast process, their standing in class rankings, and the verification diagrams they use. Student feedback on the probabilistic game is also discussed.

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Steven G. Decker

Abstract

Potential vorticity (PV) is a powerful concept in geophysical fluid dynamics. One property of PV that makes it so powerful is that it may be inverted under certain conditions, one of which is the imposition of a balance constraint. Previous studies have made use of a particular nonlinear balance constraint suited to isobaric coordinates as part of their inversion procedures. The present study constructs and tests a new nonlinear balance constraint that may be applied directly to the output of the Weather Research and Forecasting (WRF) model on its native terrain-following vertical coordinate. Output from the nonlinear balance operator is examined in the context of idealized and real-data WRF forecasts, and the results indicate that the simplifications necessary to derive the nonlinear balance operator are justified on the synoptic and meso-α scales. On the other hand, once the scales resolved by the model are small enough, neglected terms reach magnitudes on the order of the retained terms, even over flat terrain. This suggests that the use of this operator within a PV inversion scheme that also uses the WRF vertical coordinate would not capture a divergent portion of the flow that may be significant.

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Steven G. Decker and David A. Robinson

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This study presents the first evidence for the occurrence of a downslope windstorm in New Jersey. During the early morning hours of 4 January 2009, an unanticipated strong wind event was observed. Despite a zone forecast calling for winds less than 4 m s−1 issued 4 h prior to the event, winds up to 23 m s−1 were reported at High Point, New Jersey (elevation 550 m), with gusts to 30 m s−1 in its immediate lee (elevation 311 m). These winds were highly localized; a nearby Automated Surface Observing System (ASOS) station (Sussex, New Jersey, 12 km distant) reported calm winds between 0700 and 1000 UTC, just as the winds were peaking near High Point. High Point is the highest point in New Jersey, and is part of the quasi-two-dimensional Kittatinny Mountain extending from Pennsylvania into New York. This study tests the hypothesis that the topography of High Point, upon interacting with the local atmospheric stability and wind profiles, was sufficient to produce a downslope windstorm, thus causing these unusual winds. The results indicate that the presence of a sharp low-level temperature inversion in combination with a northwesterly low-level jet perpendicular to the ridge provided the key ingredients for the strong winds. Linear theory does not appear to explain the winds. Instead, prior studies incorporating nonlinearity predict a trapped lee wave or possibly a hydraulic jump, and model simulations suggest that High Point was indeed tall enough to generate such a wave along with rotors, although observations were not available to confirm this. Given sufficient model resolution, many aspects of this event were predictable. Similar windstorms have occurred before at High Point, but observations show that this event was the most amplified in recent years.

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Steven G. Decker and Jonathan E. Martin

Abstract

Local energetics diagnostics of the life cycles of consecutive, explosively deepening, extratropical cyclones that migrated across central North America in April 2001 are presented. Both storms developed rapidly and followed nearly identical tracks through the region. Despite similar mature-stage intensities, the two storms underwent vastly different evolutions during cyclolysis; the first decayed as rapidly as it had developed, and the second decayed very slowly. Examination of the volume-integrated eddy kinetic energy (EKE) budget for each storm reveals that the sea level pressure minimum associated with the first cyclone developed well after its associated EKE center had reached its maximum intensity. In contrast, the second cyclone’s sea level pressure minimum developed much more in concert with the development of its associated EKE center. As a consequence, the first cyclone began losing EKE through downstream energy fluxes even as it was developing at the surface, whereas the second cyclone did not disperse EKE downstream until later in its life cycle. Consideration of the EKE budget results in terms of baroclinic wave packets demonstrates that the first cyclone developed and decayed on the upstream edge of a wave packet, whereas the second cyclone developed in the midst of a wave packet, only decaying once it had reached the upstream edge. Thus, it is suggested that postmature phase decay is dynamically linked to a cyclone’s position in a given wave packet.

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Stephen D. Nicholls and Steven G. Decker

Abstract

The impact of ocean–atmosphere coupling and its possible seasonal dependence upon Weather Research and Forecasting (WRF) Model simulations of seven, wintertime cyclone events was investigated. Model simulations were identical aside from the degree of ocean model coupling (static SSTs, 1D mixed layer model, full-physics 3D ocean model). Both 1D and 3D ocean model coupling simulations show that SSTs following the passage of a nor’easter did tend to cool more strongly during the early season (October–December) and were more likely to warm late in the season (February–April). Model simulations produce SST differences of up to 1.14 K, but this change did not lead to significant changes in storm track (<100 km), maximum 10-m winds (<2 m s−1), or minimum sea level pressure (≤5 hPa). Simulated precipitation showed little sensitivity to model coupling, but all simulations did tend to overpredict precipitation extent (bias > 1) and have low-to-moderate threat scores (0.31–0.59). Analysis of the storm environment and the overall simulation failed to reveal any statistically significant differences in model error attributable to ocean–atmosphere coupling. Despite this result, ocean model coupling can reduce dynamical field error at a single level by up to 20%, and this was slightly greater (1%–2%) with 3D ocean model coupling as compared to 1D ocean model coupling. Thus, while 3D ocean model coupling tended to generally produce more realistic simulations, its impact would likely be more profound for longer-term simulations.

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Brian J. Cerruti and Steven G. Decker

A local winter storm scale (LWSS) is developed to categorize the disruption caused by winter storms using archived surface weather observations from a single location along the U.S. East Coast. Development of LWSS is motivated by the recognition that the observed societal impact from a given winter storm (called realized disruption here) arises from the convolution of two factors, the meteorological conditions that lead to disruption (i.e., intrinsic disruption) and society's susceptibility to winter weather. LWSS is designed to measure the first factor, intrinsic disruption. The scale uses maximum sustained winds, wind gusts, storm-total snowfall and icing accumulations, and minimum visibility to arrive at a categorical value between 0 and 5 inclusive. An alternate method is used to quantify the realized disruption that each storm produced and helps calibrate aspects of LWSS. All winter storms observed at Newark Liberty International Airport over the 15 cold seasons between 1995/96 and 2009/10 were categorized using LWSS. Focusing on one location reduces the variability in societal susceptibility, which allows the relationship between intrinsic disruption and realized disruption to be quantified.

Some important factors related to societal susceptibility were found to increase storms' realized disruption, including occurrence during a weekday, off-peak season, and less than two days subsequent to a previous storm. A closer examination of the 9–11 February 2010 winter storm demonstrates LWSS's ability to depict the spatial variability of the storm's intrinsic disruption. This information is used to infer variations in societal susceptibility between metropolitan areas and reveals the need for an instantaneous intrinsic disruption index to account for temporal variations in storm intensity.

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Brian J. Cerruti and Steven G. Decker

Abstract

A generalized linear model (GLM) has been developed to relate meteorological conditions to damages incurred by the outdoor electrical equipment of Public Service Electric and Gas, the largest public utility in New Jersey. Utilizing a perfect-prognosis approach, the model consists of equations derived from a backward-eliminated multiple-linear-regression analysis of observed electrical equipment damage as the predictand and corresponding surface observations from a variety of sources including local storm reports as the predictors. Weather modes, defined objectively by surface observations, provided stratification of the data and served to increase correlations between the predictand and predictors. The resulting regression equations produced coefficients of determination up to 0.855, with the lowest values for the heat and cold modes, and the highest values for the thunderstorm and mix modes. The appropriate GLM equations were applied to an independent dataset for model validation, and the GLM shows skill [i.e., Heidke skill score (HSS) values greater than 0] at predicting various thresholds of total accumulated equipment damage. The GLM shows higher HSS values relative to a climatological approach and a baseline regression model. Two case studies analyzed to critique model performance yielded insight into GLM shortcomings, with lightning information and wind duration being found to be important missing predictors under certain circumstances.

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Anthony M. DeAngelis, Anthony J. Broccoli, and Steven G. Decker

Abstract

Climate model simulations of daily precipitation statistics from the third phase of the Coupled Model Intercomparison Project (CMIP3) were evaluated against precipitation observations from North America over the period 1979–99. The evaluation revealed that the models underestimate the intensity of heavy and extreme precipitation along the Pacific coast, southeastern United States, and southern Mexico, and these biases are robust among the models. The models also overestimate the intensity of light precipitation events over much of North America, resulting in fairly realistic mean precipitation in many places. In contrast, heavy precipitation is simulated realistically over northern and eastern Canada, as is the seasonal cycle of heavy precipitation over a majority of North America. An evaluation of the simulated atmospheric dynamics and thermodynamics associated with extreme precipitation events was also conducted using the North American Regional Reanalysis (NARR). The models were found to capture the large-scale physical mechanisms that generate extreme precipitation realistically, although they tend to overestimate the strength of the associated atmospheric circulation features. This suggests that climate model deficiencies such as insufficient spatial resolution, inadequate representation of convective precipitation, and overly smoothed topography may be more important for biases in simulated heavy precipitation than errors in the large-scale circulation during extreme events.

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William J. Gutowski Jr., Steven G. Decker, Rodney A. Donavon, Zaitao Pan, Raymond W. Arritt, and Eugene S. Takle

Abstract

Precipitation intensity spectra for a central U.S. region in a 10-yr regional climate simulation are compared to corresponding observed spectra for precipitation accumulation periods ranging from 6 h to 10 days. Model agreement with observations depends on the length of the precipitation accumulation period, with similar results for both warm and cold halves of the year. For 6- and 12-h accumulation periods, simulated and observed spectra show little overlap. For daily and longer accumulation periods, the spectra are similar for moderate precipitation rates, though the model produces too many low-intensity precipitation events and too few high-intensity precipitation events for all accumulation periods. The spatial correlation of simulated and observed precipitation events indicates that the model's 50-km grid spacing is too coarse to simulate well high-intensity events. Spatial correlations with and without very light precipitation indicate that coarse resolution is not a direct cause of excessive low-intensity events. The model shows less spread than observations in its pattern of spatial correlation versus distance, suggesting that resolved model circulation patterns producing 6-hourly precipitation are limited in the range of precipitation patterns they can produce compared to the real world. The correlations also indicate that replicating observed precipitation intensity distributions for 6-h accumulation periods requires grid spacing smaller than about 15 km, suggesting that models with grid spacing substantially larger than this will be unable to simulate the observed diurnal cycle of precipitation.

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