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The damage potential of a tropical cyclone is proportional to a power (generally greater than one) of intensity, which demands high accuracy in forecasting intensity for managing this natural disaster. However, the current skill in forecasting cyclone intensity is rather limited, especially over the north Indian Ocean, with very little improvement over the years. A methodology is presented here for objective nonlinear debiasing to generate intensity forecasts with enhanced reliability from raw forecasts. The intensity forecast is generated using an optimized configuration of a variable resolution global circulation model (VR-GCM) that combines the advantages of a limited area model and a global model. The hindcasts were carried out in a completely operational setting, that is, without assuming any observed information beyond the day of the initial condition. The VR-GCM and a nonlinear debiasing were found to provide skill (skill score ~0.5) in forecasting tropical cyclone intensity 2--7 days (variable depending on event) in advance for the 30 cases including storms and cyclones representing different locations, seasons, and years (1990--2005) over the Bay of Bengal. Two types of debiasing are considered: nonlinear debiasing with all observations (potential skill) and nonlinear debiasing for realizable skill (training without in-sample data). It is shown that while skill scores without debiasing are only marginally better than a climatological forecast (null hypothesis), the skill score with a nonlinear debiasing is appreciable. The climatological forecast has zero skill score, a mean absolute error of 12.9 m s−−1, and 20%% of the cases are in the error bin −−5 to ++5 m s−−1; the corresponding numbers for debiased forecasts for realizable skill are 0.65, 6.4 m s−−1, and 57%%. It is further shown that the nonlinear debiasing is also effective in improving forecast of (3-hourly) intensity change. While a strict comparison of skill with other methods requires experiments to be carried out for the same events, a comparison of the skill of other methods and over different ocean basins based on available data shows the present method to have comparable skill. However, it may be noted that the present conclusions are based on a relatively small (30 events) sample size; evaluation with a much large sample size is desirable for actual application. [ABSTRACT FROM AUTHOR]

The urban peoples of the Swahili coast traded across eastern Africa and the Indian Ocean and were among the first practitioners of Islam among sub-Saharan people 1,2 . The extent to which these early interactions between Africans and non-Africans were accompanied by genetic exchange remains unknown. Here we report ancient DNA data for 80 individuals from 6 medieval and early modern (AD 1250-1800) coastal towns and an inland town after AD 1650. More than half of the DNA of many of the individuals from coastal towns originates from primarily female ancestors from Africa, with a large proportion-and occasionally more than half-of the DNA coming from Asian ancestors. The Asian ancestry includes components associated with Persia and India, with 80-90% of the Asian DNA originating from Persian men. Peoples of African and Asian origins began to mix by about AD 1000, coinciding with the large-scale adoption of Islam. Before about AD 1500, the Southwest Asian ancestry was mainly Persian-related, consistent with the narrative of the Kilwa Chronicle, the oldest history told by people of the Swahili coast 3 . After this time, the sources of DNA became increasingly Arabian, consistent with evidence of growing interactions with southern Arabia 4 . Subsequent interactions with Asian and African people further changed the ancestry of present-day people of the Swahili coast in relation to the medieval individuals whose DNA we sequenced., (© 2023. The Author(s).)