extRemes 2.0: An Extreme Value Analysis Package in R

This article describes the extreme value analysis (EVA) R package extRemes version 2.0, which is completely redesigned from previous versions. The functions primarily provide utilities for implementing univariate EVA, with a focus on weather and climate applications, including the incorporation of covariates, as well as some functionality for assessing bivariate tail dependence

Spatio-temporal models for large-scale indicators of extreme weather

The changing global climate has sparked an interest in how these changes are affecting the intensity and frequency of extreme weather events such as thunderstorms and tornadoes because these extreme events pose a significant threat to life, property, and economic stability. This article uses and evaluates several spatio-temporal statistical extreme value models to model extreme weather from reanalysis data observed across the continental United States and Mexico. The models find that the intensity of extreme weather is particularly high for the central United States. Additionally, the intensity of extreme weather is increasing over time but the amount of increase may not be practically significant

2010: Confidence intervals for forecast verification

The Technical Notes series provides an outlet for a variety of NCAR Manuscripts that contribute in specialized ways to the body of scientific knowledge but that are not suitable for journal, monograph, or book publication. Reports in this series are issued by the NCAR scientific divisions. Designation symbols for the series include: EDD – Engineering, Design, or Development Reports Equipment descriptions, test results, instrumentation, and operating and maintenance manuals. IA – Instructional Aids Instruction manuals, bibliographies, film supplements, and other research or instructional aids. PPR – Program Progress Reports Field program reports, interim and working reports, survey reports, and plans for experiments. PROC – Proceeding

Optimizing METAR Network Design for Verification of Cloud Ceiling Height and Visibility Forecasts

Methods are given and explored for thinning METAR stations in order to make more meaningful verification analyses of cloud ceiling and visibility forecasts for use within the general aviation community. Verification of these forecasts is performed based on data from surface METAR stations, which for some areas are densely located and others only sparsely located. Forecasts, which are made over an entire grid, may be awarded or penalized multiple times for a correct or incorrect forecast if there are many METAR stations situated closely together. A coverage design technique in conjunction with a percent of agreement analysis is used to find an “optimal ” network design in order to better score forecasts over densely located regions. Preliminary results for a network of 104 monitors in the New England area suggest that the removal of some stations is appropriate.