How to judge the quality and value of weather forecast products

By J. Thornes, David B. Stephenson. 2001

158 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/97ad4386dc78ce96386b2616f7209437aac03a1d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

In Global and Planetary Change

Citations in the corpus

Assessment of GPM-IMERG and Other Precipitation Products against Gauge Data under Different Topographic and Climatic Conditions in Iran: Preliminary Results (2016, background): “The range is −1 to 1, while 0 indicates no skill and 1 is the perfect score [35–37].”
Sensitivity of Several Performance Measures to Displacement Error, Bias, and Event Frequency (2006, background): “The opposite is true for users sensitive to false alarm errors; forecasts with low bias generally provide more value than unbiased forecasts Thornes and Stephenson (2001) provided an example of the complicated relationship between forecast bias, accuracy, and value for a winter weather forecasting…”
The economic value of ensemble forecasts as a tool for risk assessment: From days to decades (2002, background): “There are indications that it is not: for example, Murphy (1977), Roebber and Bosart (1996) and Thornes and Stephenson (2001) all describe applications where C=L ¿ 1.”
Rationality for Mortals: How People Cope with Uncertainty (2008, background): “Only a few of them confused probabilities with odds (“the odds are 3 to 10”; on odds see Thornes & Stephenson, 2001).”
Comparing Probabilistic Forecasting Systems with the Brier Score (2007, background): “Woodcock (1976), Seaman et al. (1996), Kane and Brown (2000), Stephenson (2000), Thornes and Stephenson (2001), and Wilks (2006, section 7.9) propose confidence intervals for scores of deterministic binary-event forecasts.”
Uncertainty and Inference for Verification Measures (2007, background): “Forecasting), Hamill (1999), Raftery et al. (2005), Seaman et al. (1996), Stephenson (2000), Thornes and Stephenson (2001), and Woodcock (1976), together with some other references that are cited later in the paper.”
Sampling Uncertainty and Confidence Intervals for the Brier Score and Brier Skill Score (2008, background): “…such as the probability of detection or hit rate (Kane and Brown 2000; Jolliffe 2007), odds ratio (Stephenson 2000), false alarm rate and skill scores (Thornes and Stephenson 2001), the correlation coefficient (Jolliffe 2007), and relative operating characteristics (Mason and Graham 2002).”
The rationale behind the success of multi-model ensembles in seasonal forecasting — II. Calibration and combination (2005, background): “Although all these measures are thoroughly described in Thornes and Stephenson (2001) , Jolliffe and Stephenson (2003), Doblas-Reyes et al. (2003) and Mason (2004), a brief description can be found in Appendix A.”