THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences

Cardiovascular Magnetic Resonance in Nonischemic Myocardial Inflammation: Expert Recommendations.

The development of NWP models with grid spacing down to ∼1 km should produce more realistic forecasts of convective storms. However, greater realism does not necessarily mean more accurate precipitation forecasts. The rapid growth of errors on small scales in conjunction with preexisting errors on larger scales may limit the usefulness of such models. The purpose of this paper is to examine whether improved model resolution alone is able to produce more skillful precipitation forecasts on useful scales, and how the skill varies with spatial scale. A verification method will be described in which skill is determined from a comparison of rainfall forecasts with radar using fractional coverage over different sized areas. The Met Office Unified Model was run with grid spacings of 12, 4, and 1 km for 10 days in which convection occurred during the summers of 2003 and 2004. All forecasts were run from 12-km initial states for a clean comparison. The results show that the 1-km model was the most skillfu...

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Scale-Selective Verification of Rainfall Accumulations from High-Resolution Forecasts of Convective Events

Morphometric tools for landmark data: geometry and biology

The receiver operating characteristic (ROC) curve is a two-dimensional measure of classification performance The area under the ROC curve (AUC) is a scalar measure gauging one facet of performance In this short article, five idealized models are utilized to relate the shape of the ROC curve, and the area under it, to features of the underlying distribution of forecasts This allows for an interpretation of the former in terms of the latter The analysis is pedagogical in that many of the findings are already known in more general (and more realistic) settings; however, the simplicity of the models considered here allows for a clear exposition of the relation For example, although in general there are many reasons for an asymmetric ROC curve, the models considered here clearly illustrate that an asymmetry in the ROC curve can be attributed to unequal widths of the distributions Furthermore, it is shown that AUC discriminates well between “good” and “bad” models, but not between good models

The ROC Curve and the Area under It as Performance Measures

The National Severe Storms Laboratory's (NSSL) mesocyclone detection algorithm (MDA) is designed to scotch for patterns in Doppler velocity radar data that are associated with rotating updrafts in severe thunderstorms. These storm-scale circulations are typically precursors to tornados and severe weather in thunderstorms, yet not all circulations produce such phenomena. A neural network has been designed to diagnose which circulations detected by the NSSL MDA yield tornados. The data used both for the training and the testing of the network are obtained from the NSSL MDA. In particular, 23 variables characterizing the circulations are selected to be used as the input nodes of a feed-forward neural network. The output of the network is chosen to be the existence/nonexistence of tornados, based on ground observations. It is shown that the network outperforms the rule-based algorithm existing in the MDA, as well as statistical techniques such as discriminant analysis and logistic regression. Additio...

A Neural Network for Tornado Prediction Based on Doppler Radar-Derived Attributes

How can environmental scientists and engineers use the increasing amount of available data to enhance our understanding of planet Earth, its systems and processes? This book describes various potential approaches based on artificial intelligence (AI) techniques, including neural networks, decision trees, genetic algorithms and fuzzy logic. Part I contains a series of tutorials describing the methods and the important considerations in applying them. In Part II, many practical examples illustrate the power of these techniques on actual environmental problems. International experts bring to life ways to apply AI to problems in the environmental sciences. While one culture entwines ideas with a thread, another links them with a red line. Thus, a red thread ties the book together, weaving a tapestry that pictures the natural data-driven AI methods in the light of the more traditional modeling techniques, and demonstrating the power of these data-based methods.

Artificial Intelligence Methods in the Environmental Sciences

A set of 14 scalar, nonprobabilistic measures—some old, some new—is examined in the rare-event situation. The set includes measures of accuracy, association, discrimination, bias, and skill. It is found that all measures considered herein are inequitable in that they induce under- or overforecasting. One condition under which such bias is not induced (for some of the measures) is when the underlying class-conditional distributions are Gaussian (normal) and equivariant.

Scalar measures of performance in rare-event situations

The National Severe Storms Laboratory has developed algorithms that compute a number of Doppler radar and environmental attributes known to be relevant for the detection/prediction of severe hail. Based on these attributes, two neural networks have been developed for the estimation of severe-hail size: one for predicting the severe-hail size in a physical dimension, and another for assigning a probability of belonging to one of three hail size classes. Performance is assessed in terms of multidimensional (i.e., nonscalar) measures. It is shown that the network designed to predict severe-hail size outperforms the existing method for predicting severe-hail size. Although the network designed for classifying severe-hail size produces highly reliable and discriminatory probabilities for two of the three hail-size classes (the smallest and the largest), forecasts of midsize hail, though highly reliable, are mostly nondiscriminatory.

Caren Marzban

Papers

The ROC Curve and the Area under It as Performance Measures

A Neural Network for Tornado Prediction Based on Doppler Radar-Derived Attributes

Artificial Intelligence Methods in the Environmental Sciences

Scalar measures of performance in rare-event situations

A Bayesian Neural Network for Severe-Hail Size Prediction