This report is a result of research into the use of CAPE as a representation of convective instability in the atmosphere and convective clouds. These clouds would result in differing values of shortwave radiation (SW) and could produce a possible systematic error in reanalysis data. Through post-processing this systematic error could be reduced to create a better representation of SW in reanalysis over Ireland.
A climatology of CAPE for Great Britain found three main CAPE seasons:
‘land dominated CAPE’ between April and September, ‘Sea dominated CAPE’ between September and January and ‘low CAPE’ from January to April [Holley et al., 2014]. Similar results were found for Ireland.
Initial data analysis found no clear conclusions in correlations or analysis of large SW error events. CAPE is highly variable both spatially and temporally. As the ability of a model to accurately simulate CAPE
depends heavily on the vertical grid spacing in the critical layers, the plan is to return to this research later with a higher resolution model.
EUMETNET = European Meteorological Services Network
is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. All Members are National Meteorological (and Hydrological) Services of their respective countries. The current list of Members can be found here and in the map below.
The 2017 brochure can be found here. Activities include:
(Published in the International Journal of Climatology in 2006)
This article identified some statistical linkage existing between climate variability patterns and the frequency of atmospheric blocking over the Euro-Atlantic region.
The research of this article started by defining three different blocking indices, i.e., indices that capture the occurrence of blocking episodes according to a given set of atmospheric conditions over time. One of these blocking indices is based on meridional gradients of Geopotential Height. The other two are based on vertical gradients of Potential Vorticity. The different characteristics of these (and other) blocking indices offer more robustness to the results shown by the research.
Those indices, applied to a gridded dataset, can express the frequency of blocking over the respective geographic domain. These types of outputs can be compared with other spatial features, in an attempt to find any links that help describe blocking episodes. This paper assessed blocking frequency against the indices of the Winter climate patterns of the region, following tips by Pavan et al (2000) and Stein et al (2000); and better described not only the relationship between blocking and the North Atlantic Oscillation, henceforth NAO (primary mode of variability in the region), but for also climate patterns. In sum:
Anti-correlation between the NAO index signal and blocking frequency in the Atlantic.
Correlation between the NAO index signal and blocking frequency in Central and Western Europe.
This shows that the NAO-blocking relationship strongly depends on location.
Blocking occurrence was also associated with other Winter climate variability pattern, specially, the secondary (EOF2) and tertiary (EOF3) climate patterns (sometimes called the East Atlantic pattern and the Scandinavian pattern). The authors highlight how the Scandinavian pattern (EOF3) is consistently related with blocking over the North Sea and the Norwegian sea.
Relevancy for our research:
– If atmospheric blocking in this region is, to some extent, dependent on the various Winter climate patterns’s phase, then some success in forecasting atmospheric blocking may be attained by further understanding the Winter climate patterns, some of which have been shown to be somewhat predictable months ahead.
– Understanding if, and how, peaks/lows of renewable energy generation (e.g. wind and solar) are related to blocking events may help improve energy forecasting.
Pavan et al. (2000)– “Seasonal prediction of blocking frequency: Results from winter ensemble experiments”
Stein (2000) – “The variability of Atlantic–European blocking as derived from long SLP time series”
I had a great trip to the Wind Energy Science Conference 2017 last week (28 June 2017) in DTU, Copenhagen, where I saw lots of interesting presentations about wind power forecasting.
I gave a talk about our ESIPP research, comparing wind and solar observation data to reanalysis data and forecast data. I made the point that reanalysis data are not always in good agreement with local observations, and that sometimes forecasts at a higher resolution can outperform reanalysis at a lower resolution, even though the latter are driven by observations. This shows the importance of surface-driven (high-resolution) data on skill.