AIRS和AMSU探测产品在区域数值天气模拟中的应用研究

Shen-Cha Hsu, Chian‐Yi Liu, Szu-Chen Kuo
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引用次数: 0

摘要

数值天气预报模式的初始条件和边界条件至关重要。众所周知,卫星观测可以克服地形的限制,特别是在难以进行常规观测的海洋上空。因此,卫星数据的使用将有望改善那些缺乏传统观测的地区。搭载在NASA的EOS Aqua卫星上的先进微波探测单元(AMSU)和大气红外探测器(AIRS)分别代表微波和高光谱红外观测。两者都可以提供具有互补特性的大气温度和湿度探测。例如,AMSU具有提供多云检索的优势,而AIRS由于其更精细的高空间分辨率而可能保留大气梯度。本研究采用天气研究与预报(WRF)模式和社区网格点统计插值(GSI)资料同化系统,对AMSU/AIRS反演资料在台湾地区强降水中的应用进行评估。正面,UTC 2016/01/05 22Z,被选择来展示使用探测数据的好处。初步结果表明对总可降水量有积极影响,但时间斜率有待进一步研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Investigation of AIRS and AMSU sounding products in regional numerical weather simulation
The initial and boundary conditions are critical to the numerical weather prediction (NWP) model. It is known that satellite observations can overcome the limitations of the terrain, especially over the oceans where conventional observations are difficult to obtain. Therefore, the use of satellite data will expect to improve those regions where lack of traditional observation. The Advanced Microwave Sounding Unit (AMSU) and Atmospheric InfraRed Sounder (AIRS) onboard NASA’s EOS Aqua satellite, represent microwave and hyperspectral infrared observations, respectively. Both of them may provide atmospheric temperature and moisture soundings with complementary characteristics. For example, AMSU has the advantage to give cloudy retrievals while AIRS may retain the atmospheric gradient due to its finer high spatial resolution. Both data could estimate atmospheric thermodynamic state with substantial accuracy to improve high impact weather forecast In this study, we adopt the Weather Research and Forecasting (WRF) model and the community Gridpoint Statistical Interpolation (GSI) data assimilation system to evaluate the use of AMSU/AIRS retrievals for severe precipitation at Taiwan. The front, UTC 2016/01/05 22Z, is selected to demonstrate the benefit of using sounding data. The preliminary results shows a positive impact on total precipitable water while the time slope may need further investigation.
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