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Enhancement of atmospheric liquid water estimation using space-borne remote sensing data.

机译：利用星载遥感数据加强对大气液态水的估算。

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摘要

Clouds strongly affect the energy balance and water cycle, two dominant processes in the climate system. Low-level liquid clouds have the most significant influence on cloud radiative forcing due to their areal extent and frequency. Estimation of atmospheric liquid water contained in low-level clouds and the precipitation underneath them is very important in meteorology, hydrology, and climatology. Space-borne remote sensing data are widely used for global estimation of atmospheric liquid water, given that they have a wider spatial coverage than other data sources and are spanning many years. However, previous space-borne remote sensing techniques have some limitations for estimation of atmospheric liquid water in low-level liquid clouds, namely, the vertical variation of droplet effective radius (DER) is neglected in the calculation of cloud liquid water path (LWP) and the rain underneath low-level liquid clouds can be overlooked. Comprising many state-of-art passive and active instruments, the recently launched NASA A-Train series of satellites provides comprehensive simultaneous information about cloud and precipitation processes. Utilizing A-Train satellite data and ship-borne data from the East Pacific Investigation of Climate (EPIC) campaign, in this study investigated is the estimation of liquid water in low-level liquid clouds, and assessed is the potential of cloud microphysical parameters in the estimation of rain from low-level liquid clouds. This study demonstrates that assuming a constant cloud DER can cause biases in the calculation of LWP. It is also shown that accounting for the vertical variation of DER can reduce the mean biases. This study shows that DER generally increases with height in non-drizzling clouds, consistent with aircraft observations. It is found that in drizzling clouds, the vertical gradient of DER is significantly smaller than that in non-drizzling clouds, and it can become negative when the drizzle is heavier than approximately 0.1 mm hr-1. It is shown that the warm rain underneath low-level liquid clouds accounts for 45.0% of occurrences of rain and 27.5% of the rainfall amount over the global ocean areas. Passive microwave techniques underestimate the warm rain over oceans by nearly 48%. Among the cloud microphysical parameters, LWP calculated with DER profile shows the best potential for estimating warm rain, which is neglected by traditional techniques of precipitation estimation.
机译：云强烈影响能量平衡和水循环，这是气候系统中的两个主要过程。低空液态云由于其面积和频率而对云的辐射强迫影响最大。低层云中所含的大气液态水及其下的降水的估算在气象，水文学和气候学中非常重要。鉴于星载遥感数据比其他数据来源具有更广泛的空间覆盖范围，并且已使用多年，因此被广泛用于大气液态水的全球估算。但是，现有的星载遥感技术在估算低空液态云中的大气液态水方面存在一些局限性，即在计算云液态水路径（LWP）时忽略了液滴有效半径（DER）的垂直变化。低层液云下面的雨水可以忽略。最近发射的NASA A-Train系列卫星包含许多最先进的被动和主动仪器，可提供有关云和降水过程的全面同步信息。利用东太平洋气候调查（EPIC）计划中的A-火车卫星数据和船载数据，本研究中的研究是估算低层液云中的液态水，并评估其潜在的云微物理参数。低层液态云对降雨的估算。这项研究表明，假设恒定云DER会在LWP的计算中引起偏差。还表明，考虑到DER的垂直变化可以减少平均偏差。这项研究表明，在非滴流云中，DER通常随高度增加而增加，这与飞机观测结果一致。发现在细雨云中，DER的垂直梯度显着小于非细雨云，并且当细雨重于约0.1 mm hr-1时，DER的垂直梯度可能变为负。结果表明，在全球海洋地区，低层液云下方的暖雨占降雨的45.0％，占降雨量的27.5％。无源微波技术低估了近48％的海洋上空的暖雨。在云的微物理参数中，用DER剖面计算的LWP显示出估算暖雨的最佳潜力，而传统的降水估算技术却忽略了这一潜力。

著录项

  • 作者

    Chen, Ruiyue.;

  • 作者单位

    University of Maryland, College Park.;

  • 授予单位 University of Maryland, College Park.;
  • 学科 Atmospheric Sciences.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 100 p.
  • 总页数 100
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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