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首页> 外文期刊>Journal of Applied Meteorology and Climatology >Development of a Pressure-Precipitation Transmitter
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Development of a Pressure-Precipitation Transmitter

机译：开发压力降水变送器

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摘要

A novel method is proposed to create very long term daily precipitation data for the extreme statistics by computing very long term daily sea level pressure (SLP) with the SLP emulator (a statistical multilevel regression model) and then converting the SLP into precipitation by combining statistical downscaling methods of the analog ensemble and singular value decomposition (SVD). After a review of the SLP emulator, we present a multilevel regression model constructed for each month that is based on a time series of 1000 principal components of SLPs on global reanalysis data. Simple integration of the SLP emulator provides 100-yr daily SLP data, which are temporally interpolated into a 6-h interval. Next, the pressure-precipitation transmitter (PPT) is developed to convert 6-hourly SLP to daily precipitation. The PPT makes its first-guess estimate from a composite of time frames with analogous SLP transition patterns in the learning period. The departure of SLPs from the analog ensemble is then corrected with an SVD relationship between SLPs and precipitation. The final product showed a fairly realistic precipitation pattern, displaying temporal and spatial continuity. The annual-maximum precipitation of the estimated 100-yr data extended the tail of probability distribution of the 8-yr learning data.
机译：提出了一种新的方法，以通过使用SLP仿真器（统计多级回归模型）计算非常长期的每日海平面压力（SLP）来为极端统计创建非常长的每日降水数据，然后通过组合统计来将SLP转换为降水。模拟合奏和奇异值分解（SVD）的缩小方法。在对SLP仿真器进行审查后，我们为每个月提供了一个多级回归模型，该模型基于全局再分析数据的SLP的1000个主要组成部分的时间序列。 SLP仿真器的简单集成提供了100 yr的每日SLP数据，其在时间上插入为6-H间隔。接下来，开发压力降水变送器（PPT）以将6小时的SLP转化为每日沉淀。 PPT通过在学习期间的时间帧的复合材料中首先从时间帧的复合中估计。然后通过SLP和降水之间的SVD关系校正SLPS从模拟集合的偏离。最终产品显示出相当逼真的降水模式，显示时间和空间连续性。估计的100 yr数据的年度最大降水延伸了8年学习数据的概率分布尾部。

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