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首页> 外文会议>International Conference on Geoinformatics >A computer model to study the variability of grid-based Sea Surface Temperature (SST) values derived from AQUA/AMSRE satellite data and its influence on the onset of South West Monsoon near the Kerala coastal region in India
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A computer model to study the variability of grid-based Sea Surface Temperature (SST) values derived from AQUA/AMSRE satellite data and its influence on the onset of South West Monsoon near the Kerala coastal region in India

机译：一种研究从Aqua / Amsre卫星数据衍生的基于网格的海表面温度（SST）值变异的计算机模型及其对印度喀拉拉邦沿海地区南部季风爆发的影响

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A MATLAB-VBA program has been developed to generate contour maps of daily SST values over the whole globe pointing specifically to the Pacific Ocean region (covering −20° to 10° in latitude & 80° to 240° in longitude) affected by ENSO (El Nino & Southern Oscillation) events. Broad monthly time series of Southern Oscillation Index (SOI) and that of total pixel (25 km × 25 km resolution) numbers having SSTs distributed between 27-31°C with 1°C interval are examined for different years of 2003–08 before the onset of SW monsoon. The pre-monsoon (Jan-Apr) averages showed that only in 4 out of 38 years of data, SOI exceeded 15 in the negative scale (strong El-Nino event) and for all of these years the monsoon onset dates were delayed. For all other values of SOI ranging between −14 to +15 no definite correlation exists. While there has been no occurrence of strong El Nino or La Nina events during 2003–08, the monsoon onset dates varied widely from 18 May to 8 June over Kerala coast. Results of this study show that the pattern of SST distribution during January-May of any year provides a better link to the likely onset dates. While larger number of pixels (≫15,000–20,000) in the lower temperature band (27–28°C) produces normal monsoon onset, a smaller number (≪10,000–15,000) gives rise to anomalous onset dates.
机译：已经开发了一个MATLAB-VBA程序，以在整个地球上创造日常SST值的轮廓图，专门针对太平洋地区（覆盖-20°至10°在受到ENSo的经度影响的纬度和80°至240°）（ El Nino＆Southern振荡）活动。南部振荡指数（SOI）的广泛月度序列和总像素（25 km×25km分辨率）的数量在27-31°C之间分布在27-31°C之间，在2003-08之前的不同年份被检查SW季风发病。季风前期（APR）平均值表明，只有在38年的数据中只有4个，SOI以负量（强埃尔-NINO活动）超过15个，并且对于这些年来，季风发病日期延迟了季风发病日期。对于在-14到+15之间的SOI的所有其他值，都存在明确的相关性。虽然2003 - 08年期间没有出现强大的El Nino或La Nina事件，但季风发病日期从5月18日至6月8日在喀拉拉邦海岸活动中变化。该研究的结果表明，任何年1月至五月的SST分布模式提供了更好的链接到可能的发病日期。虽然较大数量的像素（27-28°C）在较大数量的像素（约合15,000-20,000）产生正常的季风发作，但较小的数量（«10,000-15,000）引起异常发病日期。

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