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首页> 外文期刊>Pure and Applied Geophysics >Indian Summer Monsoon Sub-seasonal Low-Level Circulation Predictability and its Association with Rainfall in a Coupled Model
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Indian Summer Monsoon Sub-seasonal Low-Level Circulation Predictability and its Association with Rainfall in a Coupled Model

机译:印度夏季季季季节性低级循环预测性及其与耦合模型降雨的关联

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AbstractThis study investigates predictability of the sub-seasonal Indian summer monsoon (ISM) circulation and its relation with rainfall variations in the coupled model National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2). Hindcasts based on CFSv2 for the period of 1982–2009 are used for detailed analysis. Though the model is capable of predicting the seasonal ISM rainfall at long lead months, the predication skill of the model for sub-seasonal rainfall in general is poor for short and long lead except for September. Rainfall over the ISM region/Indian Subcontinent is highly correlated with the low-level jet (LLJ) or Somali jet both in the observations and the model. The model displays improved skill in predicting LLJ as compared to precipitation in seasonal mean and September, whereas the model skill is poor for June and August. Detailed analysis reveals that the model LLJ variations throughout the season are overdependent on the El Ni?o-Southern Oscillation (ENSO) unlike in the observations. This is mainly responsible for the model’s low skill in predicting LLJ especially in July and August, which is the primary cause for the poor rainfall skill. Though LLJ is weak in September, the model skill is reasonably good because of its ENSO dependency both in model and the observations and which is contributed to the seasonal mean skill. Thus, to improve the skill of seasonal mean monsoon forecast, it is essential to improve the skill of individual months/sub-seasonal circulation and rainfall skill.]]>
机译:<![cdata [ <标题>抽象 ara id =“par1”>本研究调查了季节性印度夏季的可预测性季风(ISM)循环及其与降雨变化的环境预测耦合模型国家中心(NCEP)气候预测系统版本2(CFSv2)。基于CFSv2的HindCasts 1982-2009期间用于详细分析。虽然该模型能够预测长期优势的季节性ISM降雨,但除9月外,季节降雨量的模型的预测技能差。在ISM地区/印度次大陆的降雨与观测和模型中的低级喷射(LLJ)或索马里射流高度相关。与季节性均值和九月的降水相比,该模型显示了预测LLJ的提高技能,而模型技能在6月和8月差。详细分析表明,整个季节的LLJ变化模型是在观察中不同于EL NI的欧洲南部振荡(ENSO)的过度依赖性。这主要负责该模型的低技能预测LLJ,特别是在7月和8月,这是降雨量差的主要原因。虽然LLJ在9月份疲软,但模型技能合理良好,因为它在模型和观察中的忠诚和观察中,并且有助于季节性平均技能。因此,为了提高季节性平均季风预测的技能,至关重要的是改善单个月/季节性循环和降雨技能的技能。 ]]>

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