Corrections for Geostationary Cloud Liquid Water Path Using Microwave Imagery

IF 1.9 4区 地球科学 Q2 ENGINEERING, OCEAN Journal of Atmospheric and Oceanic Technology Pub Date : 2023-07-11 DOI:10.1175/jtech-d-23-0030.1
K. Smalley, M. Lebsock
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Abstract

Geostationary observations provide measurements of the cloud liquid water path (LWP), permitting continuous observation of cloud evolution throughout the daylit portion of the diurnal cycle. Relative to LWP derived from microwave imagery, these observations have biases related to scattering geometry, which systematically varies throughout the day. Therefore, we have developed a set of bias corrections using microwave LWP for the Geostationary Operational Environmental Satellites (GOES-16 and GOES-17) observations of LWP derived from retrieved cloud-optical properties. The bias corrections are defined based on scattering geometry (solar zenith, sensor zenith, and relative azimuth) and low-cloud fraction. We demonstrate that over the low-cloud regions of the northeast and southeast Pacific, these bias corrections drastically improve the characteristics of the retrieved LWP, including its regional distribution, diurnal variation, and evolution along short-time-scale Lagrangian trajectories.
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利用微波成像校正地球静止云液态水路径
地球静止观测提供了云-液-水路径(LWP)的测量,允许在昼夜周期的白天部分连续观测云的演变。相对于从微波图像中获得的LWP,这些观测结果具有与散射几何结构相关的偏差,散射几何结构在一天中系统地变化。因此,我们使用微波LWP为地球静止运行环境卫星(GOES-16和GOES-17)从检索到的云光学特性中获得的LWP观测开发了一组偏差校正。偏差校正是根据散射几何结构(太阳天顶、传感器天顶和相对方位角)和低云量定义的。我们证明,在东北太平洋和东南太平洋的低云区,这些偏差校正极大地改善了反演的LWP的特征,包括其区域分布、日变化和沿短时间尺度拉格朗日轨迹的演化。
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来源期刊
CiteScore
4.50
自引率
9.10%
发文量
135
审稿时长
3 months
期刊介绍: The Journal of Atmospheric and Oceanic Technology (JTECH) publishes research describing instrumentation and methods used in atmospheric and oceanic research, including remote sensing instruments; measurements, validation, and data analysis techniques from satellites, aircraft, balloons, and surface-based platforms; in situ instruments, measurements, and methods for data acquisition, analysis, and interpretation and assimilation in numerical models; and information systems and algorithms.
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