Affiliation:
1. Royal Netherlands Meteorological Institute, De Bilt, Netherlands
Abstract
Abstract
A new nonparametric method to correct model data is proposed. At any given point in space and time the correction is determined from “analogs” in a learning dataset. The learning dataset contains model data and simultaneous observations. The method is applied to the significant wave height dataset of the 45-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40). Comparison of the corrected data with significant wave height measurements from in situ buoy and global altimeter data shows clear improvements in bias, scatter, and quantiles in the whole range of values. Temporal inhomogeneities are also removed.
Publisher
American Meteorological Society
Subject
Atmospheric Science,Ocean Engineering
Reference19 articles.
1. Statistical properties of global significant wave heights and their use for validation.;Bauer;J. Geophys. Res.,1998
2. Intercomparison of the performance of operational wave forecasting systems with buoy data.;Bidlot;Wea. Forecasting,2002
3. Nonparametric Statistics for Stochastic Processes.;Bosq,1998
4. Caires, S., and A.Sterl, 2003a: On the estimation of return values of significant wave height data from the reanalysis of the European Centre for Medium-Range Weather Forecasts. Safety and Reliability, T. Bedford and P. H. A. J. M. van Gelder, Eds., Proc. of the European Safety and Reliability Conference, Lisse. Swets & Zeitlinger, 353–361.
5. Validation of ocean wind and wave data using triple collocation.;Caires;J. Geophys. Res.,2003
Cited by
113 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献