%0 Report %1 bett2018skill %A Bett, Philip %A Thornton, Hazel %A Troccoli, Alberto %D 2018 %I Zenodo %K ecem energy renewables seasonal skill verification myown %N D2.2.1 %R 10.5281/zenodo.1293863 %T Skill Assessment Of Energy-Relevant Climate Variables In A Selection Of Seasonal Forecast Models. Report Using Final Data Sets. %X This report describes an assessment of seasonal forecast skill of energy-relevant climate variables that are produced from a range of different seasonal climate prediction systems. In recent years, seasonal forecasting has been lauded as potentially useful for the energy sector. However, it is important to realise that for most cases across Europe, the skill depends strongly on the variable of interest (here we focus on temperature, wind speed, precipitation, irradiance, and relative humidity), the season (here, winter and summer), and the model itself. It is not the case that some of these are generally well predicted everywhere across all models. We present an assessment using seasonal hindcast data from ECMWF, Météo-France and the Met Office, obtained from the pre-operational phase of the Copernicus Climate Change Service (C3S) Climate Data Store (CDS). We assess the skill of the direct model output to establish a baseline from which forecasts could be improved, for example through more detailed statistical modelling. Results are presented as maps of skill across Europe, together with more focused skill assessments for the onshore areas of European countries. We find that, while there are many cases of significant skill in winter, it is very diverse: the Met Office, Météo-France and ECMWF models often differ in which countries have significant skill in each variable. In summer the patterns of skill are more consistent, although skill in temperature is due in part to positive trends over the hindcast periods, and there are fewer cases of significant skill overall. A cluster of temperature skill in the Carpathian Basin down to the Balkans stands out in all models. The lack of widespread skill suggests that future seasonal work within the ECEM project, on the skill of forecasting energy demand/supply metrics, should focus on methods of producing good forecasts for specific countries and variables, rather than on attempting a comprehensive assessment that includes areas/models/variables without any underlying skill. We note that, in practice, some centres use features of the large-scale circulation, such as the NAO, as a predictor to forecast the meteorological variables. Skillful forecasts of the NAO, or other drivers, could provide a means to predict many of the variables of interest here, and potentially the corresponding energy system metrics, in many countries across Europe.

@techreport{bett2018skill, abstract = {This report describes an assessment of seasonal forecast skill of energy-relevant climate variables that are produced from a range of different seasonal climate prediction systems. In recent years, seasonal forecasting has been lauded as potentially useful for the energy sector. However, it is important to realise that for most cases across Europe, the skill depends strongly on the variable of interest (here we focus on temperature, wind speed, precipitation, irradiance, and relative humidity), the season (here, winter and summer), and the model itself. It is not the case that some of these are generally well predicted everywhere across all models. We present an assessment using seasonal hindcast data from ECMWF, Météo-France and the Met Office, obtained from the pre-operational phase of the Copernicus Climate Change Service (C3S) Climate Data Store (CDS). We assess the skill of the direct model output to establish a baseline from which forecasts could be improved, for example through more detailed statistical modelling. Results are presented as maps of skill across Europe, together with more focused skill assessments for the onshore areas of European countries. We find that, while there are many cases of significant skill in winter, it is very diverse: the Met Office, Météo-France and ECMWF models often differ in which countries have significant skill in each variable. In summer the patterns of skill are more consistent, although skill in temperature is due in part to positive trends over the hindcast periods, and there are fewer cases of significant skill overall. A cluster of temperature skill in the Carpathian Basin down to the Balkans stands out in all models. The lack of widespread skill suggests that future seasonal work within the ECEM project, on the skill of forecasting energy demand/supply metrics, should focus on methods of producing good forecasts for specific countries and variables, rather than on attempting a comprehensive assessment that includes areas/models/variables without any underlying skill. We note that, in practice, some centres use features of the large-scale circulation, such as the NAO, as a predictor to forecast the meteorological variables. Skillful forecasts of the NAO, or other drivers, could provide a means to predict many of the variables of interest here, and potentially the corresponding energy system metrics, in many countries across Europe.}, added-at = {2018-06-29T14:35:01.000+0200}, author = {Bett, Philip and Thornton, Hazel and Troccoli, Alberto}, biburl = {https://www.bibsonomy.org/bibtex/267819fb2e12aaab374cdb265ac0f2103/pbett}, description = {Skill assessment of energy-relevant climate variables in a selection of seasonal forecast models. Report using final data sets. | Zenodo}, doi = {10.5281/zenodo.1293863}, institution = {Met Office}, interhash = {5e7d1fedaeddbebaaa0e2d5e889f669a}, intrahash = {67819fb2e12aaab374cdb265ac0f2103}, keywords = {ecem energy renewables seasonal skill verification myown}, language = {en}, month = jan, number = {D2.2.1}, publisher = {Zenodo}, timestamp = {2018-10-01T09:50:26.000+0200}, title = {Skill Assessment Of Energy-Relevant Climate Variables In A Selection Of Seasonal Forecast Models. Report Using Final Data Sets.}, type = {ECEM Deliverable}, year = 2018 }