CiteULike: mmeteo's Li

Heading System Design of MAV

Song Wang — 2007-05-21T22:51:41-00:00

Robotics, Automation and Mechatronics, 2006 IEEE Conference on (2006), pp. 1-5.

This article puts forward a new heading system (including heading measurement and heading control) framework of micro air vehicle (MAV) starting from practical application, then designs a plan in which three most popular heading measurement methods, magnetic heading, strapdown heading and GPS heading, are implemented in a mini system, analyzes their strong and weak points one by one, and provides a way to obtain a practical heading based on data fusion. The advantages of this way are appropriately accuracy, little calculation amount, no system model needed, and dynamic online self-calibration. The article also designs a plan for heading control of MAV on the basis of heading measurement, which has been realized in the IFLY30 MGNC (micro guidance, navigation and control) system. IFLY30 is used in the flight control system of a sort of mini fixed wing aircraft. Static and autonomous flight tests prove that the heading system designed in this paper is feasible with excellent performance

Global mean cloud feedbacks in idealized climate change experiments

MA Ringer — 2007-06-30T03:00:58-00:00

Geophys. Res. Lett., Vol. 33 (April 2006), 7718.

Global mean cloud feedbacks in ten atmosphere-only climate models are estimated in perturbed sea surface temperature (SST) experiments and the results compared to doubled CO<SUB>2</SUB> experiments using mixed-layer ocean versions of these same models. The cloud feedbacks in any given model are generally not consistent: the sign of the net cloud radiative feedback may vary according to the experimental design. However, both sets of experiments indicate that the variation of the total climate feedback across the models depends primarily on the variation of the net cloud feedback. Changes in different cloud types show much greater consistency between the two experiments for any individual model and amongst the set of models analyzed here. This suggests that the SST perturbation experiments may provide useful information on the processes associated with cloud changes which is not evident when analysis is restricted to feedbacks defined in terms of the change in cloud radiative forcing.

On the contribution of local feedback mechanisms to the range of climate sensitivity in two GCM ensembles

M Webb — 2007-06-30T02:59:43-00:00

Climate Dynamics, Vol. 27, No. 1. (19 July 2006), pp. 17-38.

Abstract Global and local feedback analysis techniques have been applied to two ensembles of mixed layer equilibrium CO2 doubling climate change experiments, from the CFMIP (Cloud Feedback Model Intercomparison Project) and QUMP (Quantifying Uncertainty in Model Predictions) projects. Neither of these new ensembles shows evidence of a statistically significant change in the ensemble mean or variance in global mean climate sensitivity when compared with the results from the mixed layer models quoted in the Third Assessment Report of the IPCC. Global mean feedback analysis of these two ensembles confirms the large contribution made by inter-model differences in cloud feedbacks to those in climate sensitivity in earlier studies; net cloud feedbacks are responsible for 66% of the inter-model variance in the total feedback in the CFMIP ensemble and 85% in the QUMP ensemble. The ensemble mean global feedback components are all statistically indistinguishable between the two ensembles, except for the clear-sky shortwave feedback which is stronger in the CFMIP ensemble. While ensemble variances of the shortwave cloud feedback and both clear-sky feedback terms are larger in CFMIP, there is considerable overlap in the cloud feedback ranges; QUMP spans 80% or more of the CFMIP ranges in longwave and shortwave cloud feedback. We introduce a local cloud feedback classification system which distinguishes different types of cloud feedbacks on the basis of the relative strengths of their longwave and shortwave components, and interpret these in terms of responses of different cloud types diagnosed by the International Satellite Cloud Climatology Project simulator. In the CFMIP ensemble, areas where low-top cloud changes constitute the largest cloud response are responsible for 59% of the contribution from cloud feedback to the variance in the total feedback. A similar figure is found for the QUMP ensemble. Areas of positive low cloud feedback (associated with reductions in low level cloud amount) contribute most to this figure in the CFMIP ensemble, while areas of negative cloud feedback (associated with increases in low level cloud amount and optical thickness) contribute most in QUMP. Classes associated with high-top cloud feedbacks are responsible for 33 and 20% of the cloud feedback contribution in CFMIP and QUMP, respectively, while classes where no particular cloud type stands out are responsible for 8 and 21%.