Search Results

You are looking at 1 - 10 of 52 items for

  • Author or Editor: Paul J. Roebber x
  • All content x
Clear All Modify Search
Paul J. Roebber

Abstract

An investigation into the manner in which forecasters adjust their reliance on particular pieces of forecast information as the large-scale flow pattern evolves into different regimes, and the relationship between those adjustments and forecast skill and value is presented. For the cold season months (December–February) of the period 1 January 1973 through 31 December 1992, a total of three regime types (identified through cluster analysis) comprising 63% of the days were identified. A framework for investigating the weighting of pieces of forecast information, based upon multiple regression techniques, was applied to National Weather Service (NWS) degree day forecasts (constructed from the 12–24-h minimum and 24–36-h maximum temperature forecasts) for this period. It was determined that substantial changes in the usage of Model Output Statistics (MOS) by NWS forecasters have occurred with the advent of the improved numerical model guidance represented by the Limited Fine Mesh (LFM) MOS, and that these changes occurred in response to improvements in the longer-range forecasts (validating 24–36 h from the initial time). However, it was also shown that this increased weighting of MOS was situation dependent and that forecast skill and value were maintained under large-scale flow regimes in which MOS was less useful through significant adjustment of forecast technique. Overall, skills were found to be lowest for flows in which either the variability of the MOS weight was highest (reflecting uncertainty in its reliability) or in which limitations of that guidance were evident. These results are then related to earlier investigations concerning the relationship between forecast skill and experience.

Full access
Paul J. Roebber

Abstract

A statistical analysis of 12 and 24 hour deepening rates for all surface lows analyzed on at least two successive NMC 12 hourly “front half” hemispheric surface charts was performed for one year of data. Both 12 and 24 hour deepening distributions showed statistically significant (at the 5% level) departures from normality, with the largest deviations occurring along the tail of the distribution associated with most rapid deepening. The sum of two normal curves of different means and standard deviations was successfully fitted to the deepening distributions, suggesting that most cases of explosive cyclogenesis are the result of some additional physical mechanism distinct from ordinary baroclinic instability.

The climatology of explosive cyclones (Sanders and Gyakum) was updated to include the 1979–82 cold seasons, and compared to the previous three-year sample. In addition, a climatology of formation positions, maximum deepening positions and dissipation positions for all cyclones in a one-year data sample was compiled. These studies indicate that the preferred regions of explosive cyclogenesis are primarily baroclinic zones, the climatological and statistical evidence therefore suggests that the explosive mechanism is a combination of the baroclinic process and some other mechanism or mechanisms.

Full access
Paul J. Roebber

Difficulties associated with the teaching of complex subjects such as the atmospheric sciences create obstacles to learning and lead to relatively high rates of student attrition. An exploration of the role of mismatches between student learning styles and that implicit in curricular design was conducted at the University of Wisconsin—Milwaukee (UWM), with the objective of identifying methods for improving student learning and retention.

Open-ended interviews were used to elicit the opinions of past and present students and faculty. These data are analyzed to meet the study objectives. Key findings include the following: attrition rates in the program are high, but consistent with published rates across the United States in engineering; the predominant learning styles of students and faculty diverge substantially; curricular design is consistent with faculty rather than student learning styles; among students, undergraduates show the largest negative responses to existing modes of operation and the most interest in change; faculty also show considerable discomfort with existing modes and substantial support for change, although their rationale for this support may differ from that of students; support for a radical reorganization of the curriculum toward a case-study-driven learning process is weak, particularly among undergraduates; increased emphasis of physical examples and case studies within the existing curricular framework is supported, both for upper-level undergraduates and graduate students. Methods for addressing these limitations within atmospheric science curricula are presented.

Full access
Paul J. Roebber

Abstract

The level of variability present in operational model simulations of marine cyclogenesis was examined. Successive forecasts valid for the same 12-h time period of analysed maximum cyclone central pressure fall from the National Meteorological Center (NMC) nested grid model (NGM) and the Canadian Meteorological Centre (CMC) regional finite element model (RFE) were examined for one cold season (November 1988–March 1989). All cyclones with an analysed 12-h maximum central pressure fall occurring within the Western Atlantic region were included in the study, comprising a total of 52 sets of 0–12-,12–24-, 24–36-, and 36–48-h forecasts.

Analyzed cyclone development spanned a wide range of intensities from no development (one case) to extraordinarily rapid development (greater than 24 mb in 12 h, 14 cases). The primary storm track was located offshore, resulting in reduced precipitation along the coastal regions. Both models tended to under-forecast cyclone development, and under-represent the clustering of cyclone activity within a narrow band offshore. This tendency became progressively stronger in the NGM with forecast range, while the behavior of the RFE was complicated by an apparent short-range (0–12-h) spin-up problem.

The degree of variability in a sequence of forecasts was not well related to the overall prediction error for either model, belying the view that consistency in successive model forecasts indicates reliability. Each model exhibited forecast of low variance with high error (consistently poor forecasts of development) and sets of high variance with low error (inconsistent, but within range of the analyzed development). The models performed more similarly with regard to their mean error characteristics for a given set of forecasts than in terms of the dispersion of that error within the forecast sequences.

Full access
Paul J. Roebber

Abstract

Evolutionary program ensembles are developed and tested for minimum temperature forecasts at Chicago, Illinois, at forecast ranges of 36, 60, 84, 108, 132, and 156 h. For all forecast ranges examined, the evolutionary program ensemble outperforms the 21-member GFS model output statistics (MOS) ensemble when considering root-mean-square error and Brier skill score. The relative advantage in root-mean-square error widens with forecast range, from 0.18°F at 36 h to 1.53°F at 156 h while the probabilistic skill remains positive throughout. At all forecast ranges, probabilistic forecasts of abnormal conditions are particularly skillful compared to the raw GFS guidance.

The evolutionary program reliance on particular forecast inputs is distinct from that obtained from considering multiple linear regression models, with less reliance on the GFS MOS temperature and more on alternative data such as upstream temperatures at the time of forecast issuance, time of year, and forecasts of wind speed, precipitation, and cloud cover. This weighting trends away from current observations and toward seasonal (climatological) measures as forecast range increases.

Using two different forms of ensemble member subselection, a Bayesian model combination calibration is tested on both ensembles. This calibration had limited effect on evolutionary program ensemble skill but was able to improve MOS ensemble performance, reducing but not eliminating the skill gap between them. The largest skill differentials occurred at the longest forecast ranges, beginning at 132 h. A hybrid, calibrated ensemble was able to provide some further increase in skill.

Full access
Paul J. Roebber

Abstract

A diagnostic study of two, successive operational model forecasts of a case of explosive cyclogenesis is presented, with the goal of understanding the rather substantial differences in the simulations. The rapid cyclogenesis, Which occurred to varying degree in both forecasts, can be explained as a moist baroclinic response to a strong 500-mb trough embedded within the polar airstream. The variability in the forecasts is related to differential growth of low-level cyclonic vorticity in association with amplification of the 500-mb vorticity gradient between the upstream trough and a locally forced downstream short-wave ridge, prior to the period of most rapid deepening. This antecedent vorticity growth was initiated by advection offshore of the east coast of North America of a tongue of stratospheric potential vorticity, identifiable in the conventional constant analysis as a weak short-wave trough at 500 mb. Once initiated, low-level development continued as a result of a self-development process involving an interaction between quasigeostrophic forcing of ascent and latent heat release; upon the arrival of the polar trough, rapid surface deepening ensued. The self-development process during the antecedent stage effectively lengthened the time scale of intensification, leading to greater increases in surface relative vorticity through vortex stretching. In addition, the upstream 500-mb trough was amplified during this period. The weaker development in the less successful simulation of this case occurred as a result of damped self-development and consequently reduced low-level vorticity and weaker cyclonic vorticity advection during the rapid deepening stage. The impact on predictability of such nonlinear interactions between process during cyclogenesis is discussed, with reference to short-and medium-range forecasts.

Full access
Paul J. Roebber

Abstract

Statistical analysis of cyclone deepening rates has been used in the past to infer distinctions between physical processes operative in cases of explosive cyclogenesis and lesser storms. This note attempts to qualify the conclusions of the previous study by analyzing cyclone deepening data from a new prospective. The results suggest that the debate concerning the relative normality of these distributions is essentially irrelevant. Significant statistical evidence is provided to suggest that midlatitude maritime cyclogenesis exhibits a fundamentally different character from continental events, and that this distinction is evident across a wide spectrum of storm intensities.

Full access
Paul J. Roebber

Abstract

Previous work has shown that evolutionary programming is an effective method for constructing skillful forecast ensembles. Here, two prototype adaptive methods are developed and tested, using minimum temperature forecast data for Chicago, Illinois, to determine whether they are capable of incorporating improvements to forecast inputs (as might occur with changes to operational forecast models and data assimilation methods) and to account for short-term changes in predictability (as might occur for particular flow regimes). Of the two methods, the mixed-mode approach, which uses a slow mode to evolve the overall ensemble structure and a fast mode to adjust coefficients, produces the best results. When presented with better operational guidance, the mixed-mode method shows a reduction of 0.57°F in root-mean-square error relative to a fixed evolutionary program ensemble. Several future investigations are needed, including the optimization of training intervals based on flow regime and improvements to the adjustment of fast-mode coefficients. Some remarks on the appropriateness of this method for other ensemble forecast problems are also provided.

Full access
Paul J. Roebber

Abstract

An ensemble forecast method using evolutionary programming, including various forms of genetic exchange, disease, mutation, and the training of solutions within ecological niches, is presented. A 2344-member ensemble generated in this way is tested for 60-h minimum temperature forecasts for Chicago, Illinois.

The ensemble forecasts are superior in both ensemble average root-mean-square error and Brier skill score to those obtained from a 21-member operational ensemble model output statistics (MOS) forecast. While both ensembles are underdispersive, spread calibration produces greater gains in probabilistic skill for the evolutionary program ensemble than for the MOS ensemble. When a Bayesian model combination calibration is used, the skill advantage for the evolutionary program ensemble relative to the MOS ensemble increases for root-mean-square error, but decreases for Brier skill score. Further improvement in root-mean-square error is obtained when the raw evolutionary program and MOS forecasts are pooled, and a new Bayesian model combination ensemble is produced.

Future extensions to the method are discussed, including those capable of producing more complex forms, those involving 1000-fold increases in training populations, and adaptive methods.

Full access
Paul J. Roebber

Abstract

A new method for producing large member ensemble forecasts, using a variation of evolutionary programming (EP), is presented. A series of increasingly complex datasets are used to demonstrate the method and its potential utility. First, EP performance is considered using training and test “atmospheric” data derived from the Lorenz low-order dynamical system. Next, a modified form of the intermediate-order Lorenz model representing 500-hPa height is used. Finally, EP performance is evaluated using real 500-hPa data and day-3 forecasts of the reforecast model. As expected, short observational records limit the potential of the EP method by preventing proper training. A kind of perfect-prog approach, in which the EP ensemble is trained using a large “observational” sample constructed from the imperfect model, is shown to be a potentially viable means of counteracting limits to the observed record. The EP ensembles are shown to outperform dynamical model ensembles at the extremes, and to be competitive with dynamical models across a wide range of values of the variables of interest. In particular, the EP ensembles improve resolution compared to dynamical model ensembles, which suggests that further skill might be obtainable by also improving reliability using additional postprocessing. Future applications of the ensemble EP method are briefly discussed.

Full access