The full covariance matrix dynamics of the KF, which describes the evolution along the analysis and forecast cycle, is replaced by the dynamics of the error variance and the diffusion tensor, which is related to the correlation length-scales. The PKF developed here has been applied to the simplified framework of advectionÁ diffusion of a passive tracer, for its use in chemical transport model assimilation. The PKF is easy to compute and computationally cost-effective than an ensemble Kalman filter (EnKF) in this context. The validation of the method is presented for a simplified 1-D advectionÁdiffusion dynamics.

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Increasing model resolution can improve the performance of a data assimilation system because it reduces model error, the system can more optimally use high-resolution observations, and with an ensemble data assimilation method the forecast error covariances are improved. However, increasing the resolution scales with a cubical increase of the computational costs. A method that can more effectively improve performance is introduced here. The novel approach called “Super-resolution data assimilation” (SRDA) is inspired from super-resolution image processing techniques and brought to the data assimilation context. Starting from a low-resolution forecast, a neural network (NN) emulates the fields to high-resolution, assimilates high-resolution observations, and scales it back up to the original resolution for running the next model step. The SRDA is tested with a quasi-geostrophic model in an idealized twin experiment for configurations where the model resolution is twice and four times lower than the reference solution from which pseudo-observations are extracted. The assimilation is performed with an Ensemble Kalman Filter. We show that SRDA outperforms both the low-resolution data assimilation approach and a version of SRDA with cubic spline interpolation instead of NN. The NN’s ability to anticipate the systematic differences between low- and high-resolution model dynamics explains the enhanced performance, in particular by correcting the difference of propagation speed of eddies. With a 25-member ensemble at low resolution, the SRDA computational overhead is 55% and the errors reduce by 40%, making the performance very close to that of the high-resolution system (52% of error reduction) that increases the cost by 800%. The reliability of the ensemble system is not degraded by SRDA.

Directional coding of hand movements is of primary importance in the proactive control of goal-directed aiming. At the same time, manual reaction times are known to be asymmetric when reaching at lateralized targets. Generally, ipsilateral movements and left hand advantages are interpreted using the classical model of interhemispheric transmission for simple visuomotor integration, but the use of this model was recently challenged when applied to reaching movements, arguing that attentional and biomechanical effects could also account for such asymmetries. In this work, we aimed at controlling both visual attention orienting and movement mechanical constraints in order to clarify the origin of manual reaction time asymmetries and hemispatial effects in the directional coding of reaching. Choice reaction time pointing tasks were assessed in two experiments in which identical movements were compared in different conditions of target lateralization and different conditions of head, eye and hand position. Results suggested that biomechanical constraints could account for hemispatial effects for movement execution but not for movement direction coding. These results are discussed in the light of models of interhemispheric cooperation and the right hemisphere dominance for spatial processing.