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The preferred structure of the interannual indian monsoon variability

机译:年际印度季风变化的首选结构

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摘要

The leading empirical orthogonal function (EOF) of the June-Sept. mean, rotational horizontal wind at 850 hPa and 200 hPa (over the region 12.5 degrees S-42.5 degrees N, 50 degrees E-100 degrees E) from 56 years (1948-2003) of reanalysis (from the National Centers for Environmental Prediction) shows strong anti-cyclonic circulation at upper levels, strong Indian Ocean cross-equatorial flow and on-shore flow over western India at lower levels . The associated principal component (PC) is correlated at the 0.75 level with the seasonal mean observed Indian Monsoon rainfall (IMR). Composite differences of vertically integrated divergence (surface to 800 hPa) and vorticity (surface to 500 hPa) between "strong'' years (PC-1 exceeds one standard deviation sigma) and "weak'' years (PC-1 less than - sigma) suggest increased rising motion and storminess over the Bay of Bengal and central India. Composite difference maps of station rainfall from the India Meteorological Department (IMD) between strong years and normal years (weak years and normal years) are statistically significant over central India, with strong (weak) years associated with increased (decreased) precipitation. In both cases the maps of rainfall anomalies are of one sign throughout India. The correlation of PC-1 with global seasonal mean SST is strong and negative over the eastern equatorial Pacific, but positive in a surrounding horse-shoe like region. Significant negative correlation occurs in the northwestern Indian Ocean. The lag/lead correlation between the NINO3 SST index and PC-1 is similar to but stronger than the NINO3/IMR correlation. Modest (but significant) negative correlation is seen when NINO3 leads PC-1 (or IMR) by one-two months. Strong negative correlation is seen when PC-1 (or IMR) leads NINO3. The projections of running five-day means of horizontal rotational winds at 850 and 200 hPa onto EOF-1 (after removing the seasonal mean for each year) were pooled for strong, normal and weak years. The strong and normal year probability distribution functions (pdfs) are nearly indistinguishable, but the weak year pdf has more weight for moderate negative values and in both extreme tails and shows some hint of bi-modality.
机译:6月至9月的领先经验正交函数(EOF)。重新分析后56年(1948-2003年)的平均旋转水平风在850 hPa和200 hPa(在12.5度S-42.5度N,50度E-100度E范围内)(来自国家环境预测中心)表现出较高水平的强反气旋环流,较低水平的印度洋强烈的越赤道水流和印度西部的陆上流。相关的主成分(PC)与印度季风降雨(IMR)的季节平均数相关联为0.75。 “强”年(PC-1超过一个标准偏差sigma)和“弱”年(PC-1小于-sigma)之间的垂直积分散度(表面到800 hPa)和涡度(表面到500 hPa)的复合差异)表明孟加拉湾和印度中部的上升运动和暴风雨增加。在印度中部,强年和正常年(弱年和正常年)之间印度气象局(IMD)站降水的综合差异图在统计上具有显着性,强(弱)年与降水增加(减少)有关。在这两种情况下,整个印度的降雨异常图都是一个标志。在赤道东太平洋,PC-1与全球季节性平均海温的相关性强且呈负相关,但在周围的马蹄形区域中呈正相关。显着的负相关发生在西北印度洋。 NINO3 SST指数和PC-1之间的滞后/超前相关性与NINO3 / IMR相关性相似但强于NINO3 / IMR相关性。当NINO3领先PC-1(或IMR)一到两个月时,观察到适度(但显着)的负相关。当PC-1(或IMR)领先NINO3时,可以看到强烈的负相关性。在强,正常和弱年,汇总了在EOF-1上以850和200 hPa运行五天的水平旋转风的平均值(除去每年的季节平均值之后)。强和正常年概率分布函数(pdf)几乎无法区分,但弱年pdf对于中等负值和两个极端都具有更大的权重,并显示出一些双峰的迹象。

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