Geographical regions of covariability in precipitation over the Kerala state are exposed using factor analysis. The results suggest that Kerala can be divided into three unique rainfall regions, each region having a similar covariance structure of annual rainfall. Stations north of 10 • N (north Kerala) fall into one group and they receive more rainfall than stations south of 10 • N (south Kerala). Group I stations receive more than 65% of the annual rainfall during the southwest monsoon period, whereas stations falling in Group II receive 25-30% of annual rainfall during the pre-monsoon and the northeast monsoon periods. The meteorology of Kerala is profoundly influenced by its orographical features, however it is difficult to make out a direct relationship between elevation and rainfall. Local features of the state as reflected in the rainfall distribution are also clearly brought out by the study.

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Abstract:Tropospheric biennial oscillation (TBO) is the tendency for a relatively strong monsoon to be followed by a weaker one, and vice versa, for the Asian-Australian monsoon system. According to this definition, TBO years include most of the El nino-Southern Oscillation (ENSO)-onset years and some other years also. It is believed that the TBO years, with and without ENSO-onset years, have differences only in the magnitude of the signal. In the present paper, the process of coupled TBO involving the Indian monsoon and the Indian Ocean are studied through composites of relatively strong minus weak monsoon years, with and without the ENSO-onset years, using National Centers for Environmental Prediction/National Centers for Atmospheric Research (NCEP/NCAR) data sets for the 1950-2004 time period. Different seasonal evolutions of anomalies for convection, Sea Surface Temperature (SST) and wind in a biennial cycle have been identified with and without the ENSO-onset years. In both these cases, a perfect biennial cycle for convection has been noted. In the non-ENSO biennial cycle, a perfect biennial cycle is obtained in the areas close to the Indian subcontinent for SST and wind. But for the ENSO-only years, the seasonal evolutions are different. The Indian Ocean dipole, which is considered an inherent factor for TBO, is absent in the non-ENSO years. The development and propagation of anomalies are also different in these two cases. It seems that factors other than ENSO are contributing to the biennial cycle in the non-ENSO years.

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To support the global restart of elective surgery, data from an international prospective cohort study of 8492 patients (69 countries) was analysed using artificial intelligence (machine learning techniques) to develop a predictive score for mortality in surgical patients with SARS-CoV-2. We found that patient rather than operation factors were the best predictors and used these to create the COVIDsurg Mortality Score (https://covidsurgrisk.app). Our data demonstrates that it is safe to restart a wide range of surgical services for selected patients.