Category: ESIPP

  • Post-processing Techniques for Renewable Energy Forecasts

    Here is a review of the most common statistical post-processing methods used in renewable energy forecasting. The main methods discussed here are: machine learning, model output statistics, Kalman filters, regression models, historical analogs and a process of post-processing depending on weather typing. post-processing

  • EMS Annual Meeting: European Conference for Applied Meteorology and Climatology 2018

    I attended EMS2018 last week in Budapest, where I presented a talk on my on-going work with Conor and Emily Gleeson from Met Éireann. The talk was titled “An Evaluation of Integrated Cloud Condensate in the HARMONIE-AROME NWP Model” as part of the session on cloud-aerosol-radiation interactions. I used MÉRA for the data and examined […]

  • Journal Club: 18-07-2017

    Article Title: Skilful seasonal predictions for the European energy industry Published in the Environmental Research Letters journal, in 2017. This article, produced by the UK’s Met Office Hadley Centre, is a follow up on previous recent research conducted by scientists from the same centre. That previous research has been able to reproduce the North Atlantic Oscillation […]

  • Deterministic Skill Scores

    To quantify the skill of forecast systems or reanalysis models, deterministic skill scores are used. This report describes some of the most common deterministic skill scores used in meteorology and energy/power forecasting and under which circumstances they are most suitable. These include, among others: Mean error, root mean square error (RMSE) and mean absolute error. […]