Explicitly Accounting for Observation Error in Categorical Verification of Forecasts
Open Access
- 1 June 2006
- journal article
- Published by American Meteorological Society in Monthly Weather Review
- Vol. 134 (6), 1600-1606
- https://doi.org/10.1175/mwr3138.1
Abstract
Given an accurate representation of errors in observations it is possible to remove the effect of those errors from categorical verification scores. The errors in the observations are treated as additive white noise that is statistically independent of the true value of the quantity being observed. This method can be applied to both probabilistic and deterministic verification where the verification method uses a categorical approach. In general this improves the apparent performance of a forecasting system, indicating that forecasting systems are often performing better than they might first appear.Keywords
This publication has 7 references indexed in Scilit:
- Effects of Observation Errors on the Statistics for Ensemble Spread and ReliabilityMonthly Weather Review, 2004
- Comments on “A Statistical Determination of the Random Observational Errors Present in Voluntary Observing Ships’ Meteorological Reports”Journal of Atmospheric and Oceanic Technology, 2001
- Interpretation of Rank Histograms for Verifying Ensemble ForecastsMonthly Weather Review, 2001
- Use of the “Odds Ratio” for Diagnosing Forecast SkillWeather and Forecasting, 2000
- On the estimation of radar rainfall error varianceAdvances in Water Resources, 1999
- A Method for Producing and Evaluating Probabilistic Forecasts from Ensemble Model IntegrationsJournal of Climate, 1996
- Deterministic Nonperiodic FlowJournal of the Atmospheric Sciences, 1963