Special section on statistics in the atmospheric sciences

Abstract

With the possible exception of gambling, meteorology, particularly precipitation forecasting, may be the area with which the general public is most familiar with probabilistic assessments of uncertainty. Despite the heavy use of stochastic models and statistical methods in weather forecasting and other areas of the atmospheric sciences, papers in these areas have traditionally been somewhat uncommon in statistics journals. We see signs of this changing in recent years and we have sought to highlight some present research directions at the interface of statistics and the atmospheric sciences in this special section.

Published in at http://dx.doi.org/10.1214/08-AOAS209 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

References in corpus (7)

• Nonstationary covariance models for global data
• On spatial extremes: with application to a rainfall problem
• Probabilistic quantitative precipitation field forecasting using a two-stage spatial model
• Spatial--temporal mesoscale modeling of rainfall intensity using gage and radar data
• Parameter estimation for computationally intensive nonlinear regression with an application to climate modeling
• Interpreting self-organizing maps through space--time data models
• Interpolating fields of carbon monoxide data using a hybrid statistical-physical model