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Verification of cloud‐fraction forecasts
Author(s) -
Hogan Robin J.,
O'Connor Ewan J.,
Illingworth Anthony J.
Publication year - 2009
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.481
Subject(s) - forecast skill , cloud fraction , cloud computing , meteorology , environmental science , metric (unit) , ceilometer , numerical weather prediction , lead time , fraction (chemistry) , scale (ratio) , lidar , computer science , radar , cloud cover , geology , remote sensing , geography , organic chemistry , marketing , telecommunications , operations management , business , chemistry , cartography , economics , operating system
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