Of all of the standard meteorological parameters collected and observed daily, sky cover is not only one of the most complex, but the one that is fairly ambiguously defined and difficult to quantify. Despite that, the implications of how cloud fraction and sky cover are understood not only impact daily weather forecasts, but also present challenges to assessing the state of the earth’s climate system. Part of the reason for this is the lack of observational methods for verifying the skill of clouds represented and parameterized in numerical models.
While human observers record sky cover as part of routine duties, the spatial coverage of such observations in the United States is relatively sparse. There is greater spatial coverage of automated observations, and essentially complete coverage from geostationary weather satellites that observe the Americas. A good analysis of sky cover reconciles differences between manual observations, automated observations, and satellite observations, through an algorithm that accounts for the strengths and weaknesses of each dataset. This work describes the decision structure for trusting and weighting these similar observations. Some of the issues addressed include: human and instrument error resulting from approximations and estimations, a deficiency in high cloud detectability using surface-based ceilometers, poorly resolved low cloud using infrared channels on space-based radiometers during overnight hours, and decreased confidence in satellite-detected cloud during stray light periods.
Using the blended sky cover analysis as the best representation of cloudiness, it is possible to compare the analysis to numerical model fields in order to assess the performance of the model and the parameterizations therein, as well as confirm or uncover additional relationships between sky cover and pertinent fields using an optimization methodology. The optimizer minimizes an affine expression of adjusted fields to the “truth” sky cover analysis. Results include discussion about how the blended sky cover analysis correlates with the cloud ice, cloud water, rain, snow, and other analysis fields from the High-Resolution Rapid Refresh (HRRR). The intent is to suggest a reasonable operational and scientific definition for sky cover and demonstrate an observational method that can bring consistency to analyses and forecasts of sky cover.
|Updated 20 December 2013|
|Space Science and Engineering Center|
University of Wisconsin-Madison