Gravitational instability and star formation in disk galaxies

We present a general star formation law where star formation rate depends upon efficiency $\alpha$, timescale $\tau$ of star formation, gas component $\sigma_{g}$ of surface mass density and a real exponent $n$. A given exponent $n$ determines $\tau$ which however yields the corresponding star formation rate. Current nominal Schmidt exponent $n_{s}$ for our model is $2<n_{s}<3$. Based on a gravitational instability parameter $Q_{A}$ and another dimensionless parameter $f_{P}\equiv (P/G\sigma_{c}^{2})^{1/2}$, where $P$ = pressure, $\sigma_{c}$ = column density of molecular clouds, we suggest a general equation for star formation rate which depends upon relative competence of the two parameters for various physical circumstances. We find that $Q_{A}$ emerges to be a better parameter for star formation scenario than Toomre Q-parameter. Star formation rate in the solar neighbourhood is found to be in good agreement with values inferred from previous studies. Under closed box approximation model, we obtain a relation between metallicity of gas and the efficiency of star formation. Our model calculations of metallicity in the solar neighbourhood agree with earlier estimates. We conclude that metallicity dispersion for stars of same age may result due to a change in efficiency through which different sample stars were processed. For no significant change of metallicity with age, we suggest that all sample stars were born with almost similar efficiency.Comment: 10 pages, 3 figures, submitted to MNRA

Evaluation of the potential of one to three SEASAT-SMMR channels in retrieving sea surface temperature

The scanning multichannel microwave radiometer (SMMR) aboard the SEASAT satellite measured emitted radiation in both horizontal and vertical polarizations at microwave frequencies of 6.6, 10.69, 18.0, 21.0 and 37.0 GHz. Retrieval algorithms, for sea surface temperature (SST) determination, from subsets of one to three SMMR channels are obtained by a two step statistical technique. The technique first selects the best subsets of a given size defined by an R2 criterion (coefficient of determination), of a given size by the application of an efficient 'leaps and bounds' technique on a statistical data base. It then performs a regression analysis on the selected subsets. The statistical data base employed a large (600) set of seasonally and geographically diverse atmospheric and surface parameters for radiative transfer calculations. The results of the study of one to three channel subset retrieval algorithms indicate the possibility of using 6.6V, 6.6H and 18V channels for SST determination from SEASAT-SMMR data