Assessment of thin cirrus and low cloud over snow by means of the maximum likelihood method

Cirrus clouds and low clouds over snow are sometimes difficult to assess by common retrieval methods. In the case of cirrus the reason is the highly variable optical depth while low clouds have approximately the same temperature and reflection properties as snow covered mountains (or plains). An empirical interactive method is described, which allows to classify with great detail clouds of the described types and to determine the fractional coverage of each cloud type as seen from the satellite. The statistical properties of the cloud classes are determined by analyzing small areas of uniform cloudiness. The algorithms applied to pairs of spectral images is the standard maximum likelihood method.     

What is climate?

The present meaning of the word $\text{climate}$ evolved simultaneously with the formulation of research priorities which are regarded as necessary to explore the causes and, consequences of observed environmental changes. Climate can be understood in its widest sense as the totallity of influences to which the biosphere is exposed. Of primary interest are changes of these environmental conditions which are caused by the variability of the $\text{climate system}$. This expression is used in the sense of a physical system in which energy conversions take place, and which is forced, e.g. by the solar input, geologic events and man's activity. The climate system is composed of five major subsystems: atmosphere, hydrosphere, land surfaces, cryosphere and biosphere. These subsystems interact in a complex non-linear manner in different time scales. Major climate determining processes are outlined and the research priorities are discussed, which are regarded essential to investigate them and their interactions.     

A hybrid,self-adjusting search algorithm for optimal space trajectory design

The aim of the present paper is to propose a hybrid, self adjusting, search algorithm for space trajectory optimization. By taking advantage of both direct and indirect methods, the present algorithm allows the finding of the optimal solution through the introduction of some new control parameters, whose number is smaller than that of the Lagrange multipliers, and whose range is bounded. Eventually, the optimal solution is determined by means of an iterative self-adjustment of the search domain occurring at “runtime”, without any correction by an external user. This new set of parameters can be found through a reduction process of the degrees of freedom, obtained through the transversality conditions before entering the search loop. Furthermore, such a process shows that Lagrange multipliers are subject to a deep symmetry mirroring the features of the state vector. The algorithm reliability and efficiency is assessed through some test cases, and by reproducing some optimal transfer trajectories: a full three-dimensional, minimum time Mars mission, an optimal transfer to Jupiter, and finally an injection into a circular Moon orbit.     

Possibilities of remote sensing to determine parameters needed for climate change studies

The second objective of GARP—climate research—comes more and more into the focus of the scientific community, and the use of satellites and spacelabs to acquire the necessary data is discussed widely. From an inspection of the results of current climate model computations it is attempted in this paper to deduce the criticality of atmospheric parameters with respect to climate and to deduce the required measuring accuracy to get useful data for further climate studies. It emerges that some quantities as the solar flux and albedo have to be determined to better than 1%, and that much improved global information about particles, clouds and gas distribution is necessary. The impact of these requirements on future satellite systems is discussed. One result is the need for comparative and calibrating spacelab missions as well as for adequate ground truth or in situ operations.