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A new algorithm is developed whereby the cloud Liquid Water Path (LWP) and the Total Precipitable Water (TPW) may be determined from SSM/I microwave radiometric data. An artificial Neural Network (NN) with a five-neuron single hidden layer with five neurons yields the best results. The NN algorithm for TPW and LWP is compared with log-linear regression algorithms developed on the same database. The results obtained on the simulated dataset are nearly twice as good with this new algorithm. In particular, this NN seems to be able to give a better fit for large values of LWP. Furthermore, in the case of TPW, a validation and comparison with conventional algorithms is presented, which is based on SSM/I measurements and collocated radiosonde observations (RAOBs). The main conclusion is that the NN algorithm is more regular than most of the other algorithms. Through this particular study, we try to elaborate a general methodology. The conclusions concern the variability of the database used to develop and test retrieval algorithms and the relevant parameters to characterize the performance of an algorithm. [ABSTRACT FROM AUTHOR]

An increasing interest in the behaviour of silicon detectors at cryogenic temperatures has been awakened by the discovery of the so-called Lazarus effect, namely the recovery of charge collection efficiency (CCE) by means of cryogenic cooling. We measured the CCEs of three single diodes previously irradiated with different neutron fluences. The current-voltage characteristic were measured at 300 and 77 K, showing that the low-temperature operation considerably decreases the steady-state current. This is also the case when a forward voltage bias is applied, which then becomes a suitable option. At 77 K, in the case of samples irradiated with 5 x 10[sup 14] neutrons cm[sup -2], the CCE is completely recovered. A third sample irradiated with 2 x 10[sup 15] neutrons cm[sup -2] shows a 60% CCE at 250 V forward bias. [ABSTRACT FROM AUTHOR]