Journal
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
Volume 123, Issue 15, Pages 7999-8016Publisher
AMER GEOPHYSICAL UNION
DOI: 10.1029/2017JD027923
Keywords
subseasonal predictions; bias correction; downscaling
Categories
Funding
- Commission for Technology and Innovation (CTI)
- NRP 70-Energy Turnaround project [407040153929]
Ask authors/readers for more resources
Subseasonal predictions bridge the gap between medium-range weather forecasts and seasonal climate predictions. This time horizon is of crucial importance for many planning purposes, including energy production and agriculture. The verification of such predictions is normally done for areal averages of upper-air parameters. Only few studies exist that verify the forecasts for surface parameters with observational stations, although this is crucial for real-world applications, which often require such predictions at specific surface locations. With this study we provide an extensive station-based verification of subseasonal forecasts against 1,637 ground based observational time series across Europe. Twenty years of temperature and precipitation reforecasts of the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System are used to analyze the period of April 1995 to March 2014. A lead time and seasonally dependent bias correction is performed to correct the daily temperature and precipitation forecasts at all stations individually. Two bias correction techniques are compared, a mean debiasing method and a quantile mapping approach. Commonly used skill scores characterizing different aspects of forecast quality are computed for weekly aggregated forecasts with lead times of 5-32days. Overall, promising skill is found for temperature in all seasons except spring. Temperature forecasts tend to show higher skill in Northern Europe and in particular around the Baltic Sea, and in winter. Bias correction is shown to be essential in enhancing the forecast skill in all four weeks for most of the stations and for both variables with QM generally performing better. Key Points
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available