Deep winds beneath Saturn's upper clouds from a seasonal long-lived planetary-scale storm

The original publication is available at www.nature.com/nature.International audienceConvective storms occur regularly in Saturn's atmosphere. Huge storms known as Great White Spots, which are ten times larger than the regular storms, are rarer and occur about once per Saturnian year (29.5 Earth years). Current models propose that the outbreak of a Great White Spot is due to moist convection induced by water. However, the generation of the global disturbance and its effect on Saturn's permanent winds have hitherto been unconstrained by data, because there was insufficient spatial resolution and temporal sampling to infer the dynamics of Saturn's weather layer (the layer in the troposphere where the cloud forms). Theoretically, it has been suggested that this phenomenon is seasonally controlled. Here we report observations of a storm at northern latitudes in the peak of a weak westward jet during the beginning of northern springtime, in accord with the seasonal cycle but earlier than expected. The storm head moved faster than the jet, was active during the two-month observation period, and triggered a planetary-scale disturbance that circled Saturn but did not significantly alter the ambient zonal winds. Numerical simulations of the phenomenon show that, as on Jupiter, Saturn's winds extend without decay deep down into the weather layer, at least to the water-cloud base at pressures of 10-12bar, which is much deeper than solar radiation penetrates

A Rescorla-Wagner Drift-Diffusion Model of Conditioning and Timing

Computational models of classical conditioning have made significant contributions to the theoretic understanding of associative learning, yet they still struggle when the temporal aspects of conditioning are taken into account. Interval timing models have contributed a rich variety of time representations and provided accurate predictions for the timing of responses, but they usually have little to say about associative learning. In this article we present a unified model of conditioning and timing that is based on the influential Rescorla-Wagner conditioning model and the more recently developed Timing Drift-Diffusion model. We test the model by simulating 10 experimental phenomena and show that it can provide an adequate account for 8, and a partial account for the other 2. We argue that the model can account for more phenomena in the chosen set than these other similar in scope models: CSC-TD, MS-TD, Learning to Time and Modular Theory. A comparison and analysis of the mechanisms in these models is provided, with a focus on the types of time representation and associative learning rule used

Retrieval validation during the European aqua thermodynamic experiment

Atmospheric and surface thermodynamic parameters retrieved with advanced hyperspectral remote sensors aboard Earth observing satellites are critical to weather prediction and scientific research. The retrieval algorithms and retrieved parameters from satellite sounders must be validated to demonstrate the capability and accuracy of both observation and data processing systems. The European Aqua Thermodynamic Experiment (EAQUATE) was conducted not only for validation of the Atmospheric InfraRed Sounder on the Aqua satellite, but also for assessment of validation systems of both ground-based and aircraft-based instruments that will be used for other satellite systems, such as the Infrared Atmospheric Sounding Interferometer on the European MetOp satellite, the Cross-track Infrared Sounder from the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project and the continuing series of NPOESS satellites. Detailed intercomparisons were conducted and presented using different retrieval methodologies: measurements from airborne ultraspectral Fourier transform spectrometers, aircraft in situ instruments, dedicated dropsondes and radiosondes, ground-based Raman lidar, as well as the European Centre for Medium-range Weather Forecasting modelled thermal structures. The results of this study not only illustrate the quality of the measurements and retrieval products, but also demonstrate the capability of the validation systems put in place to validate current and future hyperspectral sounding instruments and their scientific products

Stress modulates instrumental learning performances in horses (Equus caballus) in interaction with temperament

The present study investigates how the temperament of the animal affects the influence of acute stress on the acquisition and reacquisition processes of a learning task. After temperament was assessed, horses were subjected to a stressor before or after the acquisition session of an instrumental task. Eight days later, horses were subjected to a reacquisition session without any stressor. Stress before acquisition tended to enhance the number of successes at the beginning of the acquisition session. Eight days later, during the reacquisition session, contrary to non-stressed animals, horses stressed after acquisition, and, to a lesser extent, horses stressed before acquisition, did not improve their performance between acquisition and reacquisition sessions. Temperament influenced learning performances in stressed horses only. Particularly, locomotor activity improved performances whereas fearfulness impaired them under stressful conditions. Results suggest that direct exposure to a stressor tended to increase acquisition performances, whereas a state of stress induced by the memory of a stressor, because it has been previously associated with the learning context, impaired reacquisition performances. The negative effect of a state of stress on reacquisition performances appeared to be stronger when exposure to the stressor occurred after rather than before the acquisition session. Temperament had an impact on both acquisition and reacquisition processes, but under stressful conditions only. These results suggest that stress is necessary to reveal the influence of temperament on cognitive performances