Three short (~12 min.) presentations by the PhD students in the Weather, Climate and Energy research group.
I will present work done to assess the accuracy of different reanalyses for daily shortwave radiation (SW) values across Ireland. The high-resolution regional reanalysis, MÉRA and two low-resolution global reanalyses; ERA-Interim and MERRA2 are compared to observations for up to 30 years. Post-processing methods are explored with a view to providing an improved dataset for the renewable energy community in Ireland. The spatial pattern of SW is also assessed for the three reanalysis datasets. Daily SW varies in an east-west direction which is highlighted by the land-sea contrast. MÉRA does well in capturing this spatial pattern. ERA-Interim also captures this pattern, however it is less pronounced in MERRA2. Satellite data will be used to explore reasons for this SW spatial pattern. I will end with a discussion of future work involving adaptive spatial multivariate post-processing for the renewable energy sector in Ireland.
Combining wind and solar power production has the potential to reduce the overall variability of renewable energy generation. Skill scores for 10m wind speed are assessed for MÉRA, the high-resolution reanalysis produced by Met Éireann, and also two coarse resolution global reanalyses: ERA-Interim and MERRA2. Wind-solar correlations are compared to those calculated using observed data from stations around Ireland, across different timescales. Reanalysis datasets are found to frequently overestimate the strength of the negative correlation between wind speed and shortwave radiation. Correlations are also seen to vary with wind direction. Finally an overview will be given of future work to improve the skill of Numerical Weather Prediction models for renewable energy forecasting.
In Ireland and the UK, long-term atmospheric variability has been linked previously with large-scale atmospheric patterns such as the North Atlantic Oscillation (NAO), the East Atlantic (EA) and Scandinavian (SCAND) patterns. These patterns, identified from pressure anomalies, influence other meteorological variables relevant to renewable power generation. Further assessments of these influences are required for successful integration of renewable energy technologies in the energy grid. Using various datasets, this work explores the links between the atmospheric patterns referred to above and both wind speed and solar radiation in the region of interest. The main result of my research so far is that winter solar radiation variability is linked strongly to the NAO and SCAND patterns, and that that relationship expresses itself in zonal gradients, across both Ireland and the UK.