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Clifford F. Mass

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The coastal regions of southern Oregon and northern California can be considerably warmer than locations to the north and south when air descends the substantial mountain barrier to the east. This paper describes the event of 27 February 1985, during which Brookings, Oregon experienced the highest February temperature ever observed in that state.

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Clifford F. Mass
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Clifford F. Mass

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Clifford F. Mass

For over a half-century, the Bergen School conceptual model of cyclone structure and development has dominated the practice of synoptic meteorology, especially regarding the techniques by which surface synoptic charts are analyzed. Although the Norwegian paradigm captures some of the essential features of cyclone evolution, research and practical application over the last 60-odd years have revealed significant deficiencies, several of which are discussed in this paper. The Bergen model has also been applied in regions and under conditions quite unlike those for which the model was originally developed. Knowledge of these problems by many in the research and operational communities has had little impact on the manner in which synoptic charts are analyzed or the way the subject is described in many textbooks. Deficiencies in the underlying conceptual model of cyclone development have been compounded by a lack of consistent and well-defined procedures for defining fronts and for analyzing surface synoptic charts. Several examples of confusing and inconsistent surface analyses are presented in this paper.

To resolve these problems, the meteorological community should follow a two-pronged approach. First, the research and operational insights gained over the last half-century should be combined with recent numerical modeling and observational studies to establish improved conceptual models of cyclone evolution. Second, a clear and consistent methodology for analyzing synoptic charts should be devised. Several possible approaches for implementing these suggestions are presented in this paper.

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Clifford F. Mass

This paper describes the current application of compact discs (CD-ROM) to the storage and distribution of datasets for atmospheric sciences and related disciplines. CD-ROM technology is reviewed, currently available discs are listed, and a look at future developments is provided.

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Clifford F. Mass
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Clifford F. Mass
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F. Anthony Eckel
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Clifford F. Mass

Abstract

This study developed and evaluated a short-range ensemble forecasting (SREF) system with the goal of producing useful, mesoscale forecast probability (FP). Real-time, 0–48-h SREF predictions were produced and analyzed for 129 cases over the Pacific Northwest. Eight analyses from different operational forecast centers were used as initial conditions for running the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5).

Model error is a large source of forecast uncertainty and must be accounted for to maximize SREF utility, particularly for mesoscale, sensible weather phenomena. Although inclusion of model diversity improved FP skill (both reliability and resolution) and increased dispersion toward statistical consistency, dispersion remained inadequate. Conversely, systematic model errors (i.e., biases) must be removed from an SREF since they contribute to forecast error but not to forecast uncertainty. A grid-based, 2-week, running-mean bias correction was shown to improve FP skill through 1) better reliability by adjusting the ensemble mean toward the mean of the verifying analysis, and 2) better resolution by removing unrepresentative ensemble variance.

Comparison of the multimodel (each member uses a unique model) and varied-model (each member uses a unique version of MM5) approaches indicated that the multimodel SREF exhibited greater dispersion and superior performance. It was also found that an ensemble of unequally likely members can be skillful as long as each member occasionally performs well. Finally, smaller grid spacing led to greater ensemble spread as smaller scales of motion were modeled. This study indicates substantial utility in current SREF systems and suggests several avenues for further improvement.

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Brian C. Ancell
and
Clifford F. Mass

Abstract

Adjoint sensitivity fields have generally been viewed deterministically in the atmospheric science literature. However, uncertainty exists in the components of the adjoint model, such as the physics and the basic-state trajectories used to calculate the sensitivity fields. In this paper, the variability of adjoint sensitivity fields is examined for two collections of trajectories, each valid over a single time window: one supplied by three global operational models, and the other from a regional, operational ensemble Kalman filter system. Adjoint sensitivities are also compared using a dry adjoint and dry basic state, a moist adjoint and moist basic state, and a moist basic state and dry adjoint. The goal of these latter experiments is to explore the differences in mesoscale sensitivity fields with and without moisture, and to examine how sensitivities degrade when adjoint models utilize simplified physics. In all cases, 24-h sensitivities are produced with a low-level pressure response function over the coastal lowlands of the Pacific Northwest. Furthermore, this study examines these adjoint sensitivities at higher resolution than in previous studies. It is found that adjoint sensitivity can vary significantly in structure, magnitude, and location when different, but equally likely, basic-state trajectories are considered. Since the predicted response function shows large variance when using different basic-state trajectories, adjoint sensitivity should be viewed probabilistically. It is also found that the inclusion of moisture in both the forward and adjoint model produces significantly different sensitivity fields from the fully dry run. Furthermore, a large degradation occurs when removing moisture from the adjoint model but retaining moisture in the basic state, causing sensitivity fields to resemble that of a fully dry adjoint integration. This suggests that simplified physics relative to the forward nonlinear physics may produce significant differences from the desired “true” sensitivity field. The implications of these results for modern applications of adjoint models are discussed.

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Brian C. Ancell
and
Clifford F. Mass

Abstract

In this paper, the variation of adjoint sensitivities as horizontal and vertical resolutions are changed is investigated. The fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) and its adjoint are used with consistent physics to generate adjoint sensitivities over a 24-h period. The sensitivities are generated with respect to a response function defined as the lowest sigma level perturbation pressure over a region of northwestern Oregon. It is found that the scale, magnitude, and structure of sensitivity with respect to initial temperature varies significantly as grid spacing is decreased from 216 to 24 km. As found in other adjoint studies at relatively coarse resolution, low-level, upshear-tilted, subsynoptic-scale sensitivities were apparent, with the wavelike sensitivity pattern decreasing significantly in scale and spatial extent with increased horizontal resolution. It is also found that perturbation growth rates depend on horizontal resolution, with the adjoint sensitivities predicting larger changes in the response function with increased horizontal resolution. Relatively little change in sensitivity structure and growth rates occurred when the vertical resolution was varied from 10 to 50 vertical levels. It is shown that a majority of the predicted change in the response function comes from the very small proportion of the domain occupied by sensitive regions. Last, the accuracy of the tangent linear approximation is examined, and it is found that for perturbations made in sensitive regions, the tangent linear approximation degrades at finer grid spacing. The implications of these results are discussed for methodologies utilizing adjoint sensitivities, such as four-dimensional variational data assimilation and targeted observations strategies.

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