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Deterministic model forecasts do not convey to the end users the forecast uncertainty the models possess as a result of physics parameterizations, simplifications in model representation of physical p...
The accuracy of ensemble streamflow forecasts (ESFs) is impacted by the propagation of uncertainty associated with quantitative precipitation forecasts (QPFs) through the physical processes occurring...
The goal of this study is to compare the performances of the ensemble Kalman filter and a reduced-rank extended Kalman filter when applied to different dynamic regimes. Data assimilation experiments a...
The response of a simplified global atmospheric circulation model (PUMA) to spatiotemporal stochastic forcing is analyzed using the statistical measures originally developed for ensemble forecast eval...
The relative impact of model quality and ensemble deficiencies, on the performance of ensemble based probabilistic forecasts, is investigated from a set of idealized experiments. Data are generated ac...
We present a nonparametric approach based on local polynomial regression for ensemble forecast of time series. The state space is first reconstructed by embedding the univariate time series of the res...
We study the predictability of chaotic conservative and dissipative maps in the context of ensemble prediction. Finite-size perturbations around a reference trajectory are evolved under the full nonli...
Due to the chaotic nature of atmospheric dynamics, numerical weather prediction systems are sensitive to errors in the initial conditions. To estimate the forecast uncertainty, forecast centres produc...
This paper compares the performance of the Local Ensemble Transform Kalman Filter (LETKF) with the Physical-Space Statistical Analysis System (PSAS) under a perfect model scenario. PSAS is a 3D-Var as...
We present a novel approach to characterize and graphically represent the spatiotemporal evolution of ensembles using a simple diagram. To this aim we analyze the fluctuations obtained as differences ...
The diffuse ensemble filter     diffuse  ensemble filter       2009/10/30
A new class of ensemble filters, called the Diffuse Ensemble Filter (DEnF), is proposed in this paper. The DEnF assumes that the forecast errors orthogonal to the first guess ensemble are uncorrelated...
Analytical, purely model-based probability distributions are derived for the instantaneous global mean warming resulting from a gradual doubling of CO2 (TCR = transient climate response) and for the e...

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