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Forecasting the weather presents a unique context for statistics, blending physical modeling with complicated observational data to produce information that is used at many different levels of sophistication. We are [...]

The characteristics associated with an exponential distribution is used as a yard stick to assess distributions for 24-hr precipitation records from 13 771 rain gauges from mainly the USA and Europe. It is shown that the daily accumulated precipitation amount is approximately similar to an exponential distribution, but not identical, and that quantiles of the distributions can to a zeroth order be specified from the mean of the wet-day precipitation. However, the data have a thicker upper tail than the exponential distribution. We propose a simple method for making a crude estimate for quantiles of wet-day 24-hr precipitation distribution, and a refinement of the exponential distribution based on principal component analysis. We also show that the high quantiles are related to the wet-day mean. The associations between the wet-day mean and quantiles from the observations are compared with results from regional climate model simulations, taken from a number of regional climate models. Similar tendencies are seen in the models as in the rain gauge data. [ABSTRACT FROM AUTHOR]

Climate model experiments incorporating model runs conducted using different conditions have become popular for the study of uncertainty affecting model output and projections of climate change. Recently, such experiments have been used to also study of the uncertainties in producing high-resolution projections of climate change based on methods for dynamic downscaling of global models. In this paper, we discuss an initial analysis of a subset of the ensemble being produced by the North American Regional Climate Change Assessment Program. Using an approach based on a functional analysis of variance, we examine the differences between two different dynamic downscaling methods and demonstrate that there are significant differences between the two models and their projections of summer temperature and precipitation over North America. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This article presents the authors' comments on the article Calibrated Probabilistic Mesoscale Weather Field Forecasting: The Geostatistical Output Perturbation Period by Yulia Gel, Adrian E. Raftery and Tilmann Gneiting. Forecasting the weather presents a unique context for statistics, blending physical modeling with complicated observational data to produce information that is used at many different levels of sophistication. We are pleased that Gel, Raftery, and Gneiting (GRG) have brought this area to the attention of a statistical audience. In this discussion we give the reader a broader view of the use of ensemble techniques in numerical weather prediction (NWP). We have some comments about the use of ensembles idea presented by GRG and also present some of our recent analysis of the value of ensemble forecasts. The authors have applied elegant statistical methods to the area of ensemble forecasting and, thus, have brought it into the spotlight for the larger statistical community. Although the geostatistical output perturbation (GOP) method could be improved through the use of the richer physical content of real ensembles, we also believe that the information that the GOP ensemble delivers has a complementary value, taking the traditional model output statistics (MOS) approach one large step further by providing a spatially coherent forecast calibration.

Discusses how forecasting aircraft turbulence is based on a system of observations by pilots combined with a mostly subjective evaluation of turbulence indices derived from numerical weather prediction models. Improvements in the forecasting capability of the single indices; Use of a nonparametric multidimensional regression model and discriminant analysis to the resulting predicted values; Characteristics of a paradigm of flexible discriminant analysis (FDA); Suggestions for operational forecasts.

Presents a corrected reprint of the article "Quantifying uncertainty in projections of regional climate change: A Bayesian approach," published in a 2005 issue of the "Journal of Climate".