Abstract
The interest in trustable and accurate information about climate and its variability at local scale is currently increasing not only within the scientific community, but also by local stakeholders, political administrators and private companies. Clear, operative and close to the users’ needs climate information represent relevant support tools for a wide range of decision-making policies, including vulnerability assessment, risk management and energy production.
Seasonal forecasts, in particular, allow to provide predictions of the climate up to several months ahead and therefore they could represent precious sources of information for a wide range of activities, such as for the optimization of renewable energy sector. However, specific approaches are needed to deal with the probabilistic nature of seasonal forecasts and post-processing methods are required to adapt their large spatial resolution to the local scales of specific applications. This is particularly true for orographically complex areas, such as the Alpine regions, where coarse-resolution data could lead to remarkable under or overestimations in the predicted variables.
In this framework, we present a downscaled and bias-corrected version of seasonal forecasts provided by the ECMWF’s seasonal forecasting system (SEAS5) for temperature, precipitation and wind speed over the Alpine area and spanning the period 1983 – 2018. The approach is based on the bilinear interpolation of the 1°x1° original fields onto the target 0.25°x0.25° resolution and on the quantile-mapping procedure using ERA-5 reanalysis data for the calibration. The ERA-5 reanalysis dataset is chosen as reference in order to allow the application of the implemented scheme over different areas. The accuracy and skills of the post-processed seasonal forecast fields are evaluated, also in comparison with observations and the performance of alternative downscaling schemes.
The presented study supports the activities of the H2020 European project SECLI-FIRM on the improvement of the seasonal forecast applicability for energy production, management and assessment.