Forecast verification : a practitioner's guide in atmospheric science

By Ian T. Jolliffe, David B. Stephenson. 2011

1,395 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Environmental Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/f7e97839369dbd2024145e738a3bf2e0376375ea

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Citations in the corpus

Significance of changes in medium-range forecast scores (2016, background): “Normal chaotic error growth causes a rapid divergence between any two assimilation experiments into which even the slightest numerical perturbation is introduced just as seen by Lorenz (1963). Although the observations are the same in both experiments and they keep the analyses on a similar path, th”
Of quantiles and expectiles: consistent scoring functions, Choquet representations and forecast rankings (2015, methodology): “This is illustrated in the bottom left-hand matrix in Table 1 and corresponds to the classical, simple cost–loss decision model (Richardson, 2012).”
Ensemble Modeling of CMEs Using the WSA–ENLIL+Cone Model (2015, neutral): “In this way a reliability diagram can be constructed to determine how well the predicted probabilities of an event correspond to their observed frequencies (Wilks, 1995; Jolliffe and Stephenson, 2011).”
Detecting Improvements in Forecast Correlation Skill: Statistical Testing and Power Analysis (2017, background): “initialized in May with a resolution of ’80 km in the atmosphere (T255) and ’100 km in the ocean (18) shows a relatively good skill to forecast ENSO for the months June–July–August (JJA): the correlation with the sea surface temperature observational dataset from Merchant et al. (2014) is ray 5 0:78”
Irradiance Forecasting for the Power Prediction of Grid-Connected Photovoltaic Systems (2009, background): “A comprehensive overview on different aspects of forecast verification is given in [14].”
Prediction and monitoring of monsoon intraseasonal oscillations over Indian monsoon region in an ensemble prediction system using CFSv2 (2013, background): “Similarly the FAR is the proportion of non-occurrences that were incorrectly forecasted (Mason 2003).”