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Modern Applied Statistics With S

Abstract A two-dimensional version of the Pennsylvania State University mesoscale model has been applied to Winter Monsoon Experiment data in order to simulate the diurnally occurring convection observed over the South China Sea. The domain includes a representation of part of Borneo as well as the sea so that the model can simulate the initiation of convection. Also included in the model are parameterizations of mesoscale ice phase and moisture processes and longwave and shortwave radiation with a diurnal cycle. This allows use of the model to test the relative importance of various heating mechanisms to the stratiform cloud deck, which typically occupies several hundred kilometers of the domain. Frank and Cohen's cumulus parameterization scheme is employed to represent vital unresolved vertical transports in the convective area. The major conclusions are: Ice phase processes are important in determining the level of maximum large-scale heating and vertical motion because there is a strong anvil componen...

Numerical study of convection observed during the Winter Monsoon Experiment using a mesoscale two-dimensional model [presentation]

Summary
1In recent years the use of species distribution models by ecologists and conservation managers has increased considerably, along with an awareness of the need to provide accuracy assessment for predictions of such models. The kappa statistic is the most widely used measure for the performance of models generating presence–absence predictions, but several studies have criticized it for being inherently dependent on prevalence, and argued that this dependency introduces statistical artefacts to estimates of predictive accuracy. This criticism has been supported recently by computer simulations showing that kappa responds to the prevalence of the modelled species in a unimodal fashion.
2In this paper we provide a theoretical explanation for the observed dependence of kappa on prevalence, and introduce into ecology an alternative measure of accuracy, the true skill statistic (TSS), which corrects for this dependence while still keeping all the advantages of kappa. We also compare the responses of kappa and TSS to prevalence using empirical data, by modelling distribution patterns of 128 species of woody plant in Israel.
3The theoretical analysis shows that kappa responds in a unimodal fashion to variation in prevalence and that the level of prevalence that maximizes kappa depends on the ratio between sensitivity (the proportion of correctly predicted presences) and specificity (the proportion of correctly predicted absences). In contrast, TSS is independent of prevalence.
4When the two measures of accuracy were compared using empirical data, kappa showed a unimodal response to prevalence, in agreement with the theoretical analysis. TSS showed a decreasing linear response to prevalence, a result we interpret as reflecting true ecological phenomena rather than a statistical artefact. This interpretation is supported by the fact that a similar pattern was found for the area under the ROC curve, a measure known to be independent of prevalence.
5Synthesis and applications. Our results provide theoretical and empirical evidence that kappa, one of the most widely used measures of model performance in ecology, has serious limitations that make it unsuitable for such applications. The alternative we suggest, TSS, compensates for the shortcomings of kappa while keeping all of its advantages. We therefore recommend the TSS as a simple and intuitive measure for the performance of species distribution models when predictions are expressed as presence–absence maps.

https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/j.1365-2664.2006.01214.x

Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS)

A time-split nonhydrostatic atmospheric model for weather research and forecasting applications

List of Contributors. Preface. 1. Introduction (I. Jolliffe & D. Stephenson). 2. Basic Concepts (J. Potts). 3. Binary Events (I. Mason). 4. Categorical Events (R. Livezey). 5. Continuous Variables (M. Deque). 6. Verification of Spatial Fields (W. Drosdowsky & H. Zhang). 7. Probability and Ensemble Forecasts (Z. Toth, et al.). 8. Economic Value and Skill (D. Richardson). 9. Forecast Verification: Past, Present and Future (D. Stephenson & I. Jolliffe). Glossary. References. Author Index. Subject Index.

Forecast verification: a practitioner's guide in atmospheric science

An approach to forecasting the potential for flash flood-producing storms is developed, using the notion of basic ingredients. Heavy precipitation is the result of sustained high rainfall rates. In turn, high rainfall rates involve the rapid ascent of air containing substantial water vapor and also depend on the precipitation efficiency. The duration of an event is associated with its speed of movement and the size of the system causing the event along the direction of system movement. This leads naturally to a consideration of the meteorological processes by which these basic ingredients are brought together. A description of those processes and of the types of heavy precipitation-producing storms suggests some of the variety of ways in which heavy precipitation occurs. Since the right mixture of these ingredients can be found in a wide variety of synoptic and mesoscale situations, it is necessary to know which of the ingredients is critical in any given case. By knowing which of the ingredients...

/pdf/flash-flood-forecasting-an-ingredients-based-methodology-1l6x7t3b0b.pdf

Flash Flood Forecasting: An Ingredients-Based Methodology

Knowledge of severe local storms has been increasing rapidly in recent years as a result of both observational studies and numerical modeling experiments. This paper reviews that knowledge as it relates to development of new applications for forecasting of severe local storms. Many of these new applications are based on physical understanding of processes taking place on the storm scale and thus allow forecasters to become less dependent on empirical relationships. Refinements in pattern recognition and severe weather climatology continue to be of value to the operational severe local storms forecasters, however. Current methodology for forecasting severe local storms at the National Severe Storms Forecast Center is described. Operational uses of new forecast applications, new “real-time” data sources (such as wind profilers and Doppler radars), and improved numerical model products are discussed.

Severe Local Storms Forecasting

Severe thunderstorm evolution is synthesized, using published and unpublished studies of radar, instrumented aircraft, visual and surface observations. These observations reveal the existence of a downdraft (originating at 7–10 km AGL) on the relative upwind side of the updraft. Air decelerates at the upwind stagnation point, is forced downward and mixes with air below which then reaches the surface through evaporative cooling and precipitation drag. The initially rotating updraft is then transformed into a new mesocyclone with a divided structure, in which the circulation center lies along the zone separating the rear blank downdraft from the updraft. This process appears to result, in part, from tilting of horizontal vorticity into the vertical. It is proposed that the zone of strong vertical velocity gradient across which the mesocyclone comes to be positioned is also characterized by a strong temperature gradient and is the genesis region of strong tornadoes. Although no direct observations a...

Severe Thunderstorm Evolution and Mesocyclone Structure as Related to Tornadogenesis

The so-called True Skill Statistic (TSS) and the Heidke Skill Score (S), as used in the context of the contingency, table approach to forecast verification, are compared. It is shown that the TSS approaches the Probability of Detection (POD) whenever the forecasting is dominated by correct forecasts of non-occurrence, i.e., forecasting rare events like severe local storms. This means that the TSS is vulnerable to “hedging” in rare event forecasting. The S-statistic is shown to be superior to the TSS in this situation, accounting for correct forecasts of null events in a controlled fashion. It turns out that the TSS and S values are related in a subtle way, becoming identical when the expected values (due to chance in a k × k contingency table) remain unchanged when comparing the actual forecast table to that of a hypothetical perfect set of forecasts. Examples of the behavior of the TSS and S values in different situations are provided which support the recommendation that S be used in preference...

On Summary Measures of Skill in Rare Event Forecasting Based on Contingency Tables

A simple theoretical analysis of the impact of neglecting the virtual correction on calculation of CAPE is made. This theory suggests that while ignoring the virtual correction does not introduce much error for large CAPE values, the relative error can become substantial for small CAPE. A test of the theory is done by finding the error made by ignoring the virtual correction to CAPE for all the soundings in 1992 having positive CAPE (when the correction is made). Results of this empirical test confirm that the relative error made in ignoring the correction increases with decreasing CAPE. A number of other “corrections” to CAPE might be considered. In a discussion of the issues associated with the results of the analysis, it is recommended that CAPE calculations should include the virtual correction but that other complications should be avoided for most purposes, especially when making comparisons of CAPE values. A standardized CAPE calculation also is recommended.

/pdf/the-effect-of-neglecting-the-virtual-temperature-correction-4tp28gy4ht.pdf

Charles A. Doswell

Papers

Flash Flood Forecasting: An Ingredients-Based Methodology

Severe Local Storms Forecasting

Severe Thunderstorm Evolution and Mesocyclone Structure as Related to Tornadogenesis

On Summary Measures of Skill in Rare Event Forecasting Based on Contingency Tables

The Effect of Neglecting the Virtual Temperature Correction on CAPE Calculations