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AbstractThe scope of this work is stochastic emulation of sea level pressure (SLP) for use in error estimation and statistical prediction studies. The input SLP dataset whose statistics are to be emulated was taken from the 1979–2013 ERA-Interim dataset at full 6-hourly temporal and 0.75° spatial resolutions over the Northern Hemisphere. Upon subtracting the monthly climatological mean value and mean diurnal cycle, the SLP anomalies (SLPA) were projected onto the subspace of 1000 leading empirical orthogonal functions of the daily-mean SLPA, which account for the vast majority (u003e99%) of the full 6-hourly fields’ variance for each season. The main step of this method is the estimation of a linear autoregressive moving-average empirical model for the daily SLPA principal components (PCs) via regularized multiple linear regression; this model was driven, at the stage of simulation, by state-dependent (multiplicative) noise. Last, a diagnostic statistical scheme has been developed and implemented for accurate...

Empirical Modeling and Stochastic Simulation of Sea Level Pressure Variability