Verification of cloud‐fraction forecasts

Cloud radar and lidar can be used to evaluate the skill of numerical weather prediction models in forecasting the timing and placement of clouds, but care must be taken in choosing the appropriate metric of skill to use due to the non‐Gaussian nature of cloud‐fraction distributions. We compare the p...

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Bibliographic Details
Published in:Quarterly journal of the Royal Meteorological Society 2009-07, Vol.135 (643), p.1494-1511
Main Authors: Hogan, Robin J., O'Connor, Ewan J., Illingworth, Anthony J.
Format: Article
Language:English
Subjects:
Climatology
cloud radar
Cloudnet
Clouds
Earth, ocean, space
Exact sciences and technology
External geophysics
Lead time
Mathematical models
Meteorology
Physics of the high neutral atmosphere
Placement
skill scores
Skills
Time measurements
Weather
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Summary:Cloud radar and lidar can be used to evaluate the skill of numerical weather prediction models in forecasting the timing and placement of clouds, but care must be taken in choosing the appropriate metric of skill to use due to the non‐Gaussian nature of cloud‐fraction distributions. We compare the properties of a number of different verification measures and conclude that of existing measures the Log of Odds Ratio is the most suitable for cloud fraction. We also propose a new measure, the Symmetric Extreme Dependency Score, which has very attractive properties, being equitable (for large samples), difficult to hedge and independent of the frequency of occurrence of the quantity being verified. We then use data from five European ground‐based sites and seven forecast models, processed using the ‘Cloudnet’ analysis system, to investigate the dependence of forecast skill on cloud fraction threshold (for binary skill scores), height, horizontal scale and (for the Met Office and German Weather Service models) forecast lead time. The models are found to be least skillful at predicting the timing and placement of boundary‐layer clouds and most skilful at predicting mid‐level clouds, although in the latter case they tend to underestimate mean cloud fraction when cloud is present. It is found that skill decreases approximately inverse‐exponentially with forecast lead time, enabling a forecast ‘half‐life’ to be estimated. When considering the skill of instantaneous model snapshots, we find typical values ranging between 2.5 and 4.5 days. Copyright © 2009 Royal Meteorological Society
ISSN:0035-9009
1477-870X
1477-870X
DOI:10.1002/qj.481