Extreme events act as a catalyst for concern about whether the climate is changing. Statistical theory for extremes is used to demonstrate that the frequency of such events is relatively more dependent on any changes in the variability (more generally, the scale parameter) than in the mean (more generally, the location parameter) of climate. Moreover, this sensitivity is relatively greater the more extreme the event. These results provide additional support for the conclusions that experiments using climate models need to be designed to detect changes in climate variability, and that policy analysis should not rely on scenarios of future climate involving only changes in means.

A recently developed method of defining rain areas for the purpose of verifying precipitation produced by numerical weather prediction models is described. Precipitation objects are defined in both forecasts and observations based on a convolution (smoothing) and thresholding procedure. In an application of the new verification approach, the forecasts produced by the Weather Research and Forecasting (WRF) model are evaluated on a 22-km grid covering the continental United States during July-August 2001. Observed rainfall is derived from the stage-IV product from NCEP on a 4-km grid (averaged to a 22-km grid). It is found that the WRF produces too many large rain areas, and the spatial and temporal distribution of the rain areas reveals regional underestimates of the diurnal cycle in rain-area occurrence frequency. Objects in the two datasets are then matched according to the separation distance of their centroids. Overall, WRF rain errors exhibit no large biases in location, but do suffer from a positive size bias that maximizes during the later afternoon. This coincides with an excessive narrowing of the rainfall intensity range, consistent with the dominance of parameterized convection. Finally, matching ability has a strong dependence on object size and is interpreted as the influence of relatively predictable synoptic-scale systems on the larger areas.

Advancements in weather forecast models and their enhanced resolution have led to substantially improved and more realistic-appearing forecasts for some variables. However, traditional verification scores often indicate poor performance because of the increased small-scale variability so that the true quality of the forecasts is not always characterized well. As a result, numerous new methods for verifying these forecasts have been proposed. These new methods can mostly be classified into two overall categories: filtering methods and displacement methods. The filtering methods can be further delineated into neighborhood and scale separation, and the displacement methods can be divided into features based and field deformation. Each method gives considerably more information than the traditional scores, but it is not clear which method(s) should be used for which purpose.A verification methods intercomparison project has been established in order to glean a better understanding of the proposed methods in terms of their various characteristics and to determine what verification questions each method addresses. The study is ongoing, and preliminary qualitative results for the different approaches applied to different situations are described here. In particular, the various methods and their basic characteristics, similarities, and differences are described. In addition, several questions are addressed regarding the application of the methods and the information that they provide. These questions include (i) how the method(s) inform performance at different scales; (ii) how the methods provide information on location errors; (iii) whether the methods provide information on intensity errors and distributions; (iv) whether the methods provide information on structure errors; (v) whether the approaches have the ability to provide information about hits, misses, and false alarms; (vi) whether the methods do anything that is counterintuitive; (vii) whether the methods have selectable parameters and how sensitive the results are to parameter selection; (viii) whether the results can be easily aggregated across multiple cases; (ix) whether the methods can identify timing errors; and (x) whether confidence intervals and hypothesis tests can be readily computed.

Leading NWP centers have agreed to create a database of their operational ensemble forecasts and open access to researchers to accelerate the development of probabilistic forecasting of high-impact weather.Objectives and cOncept. During the past decade, ensemble forecasting has undergone rapid development in all parts of the world. Ensembles are now generally accepted as a reliable approach to forecast confidence estimation, especially in the case of high-impact weather. Their application to quantitative probabilistic forecasting is also increasing rapidly. In addition, there has been a strong interest in the development of multimodel ensembles, whether based on a set of single (deterministic) forecasts from different systems, or on a set of ensemble forecasts from different systems (the so-called superensemble). The hope is that multimodel ensembles will provide an affordable approach to the classical goal of increasing the hit rate for prediction of high-impact weather without increasing the false-alarm rate. This is being taken further within The Observing System Research and Predictability Experiment (THORPEX), a major component of the World Weather Research Programme (WWRP) under the World Meteorological Organization (WMO). A key goal of THORPEX is to accelerate improvements in