Neural network temperature and moisture retrieval technique for real-time processing of FengYun-4B/GIIRS hyperspectral radiances

IF 1.9 4区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES
Hui Liu, Wenguang Bai, Gang Ma, Gang Wang, Peng Zhang, Wenjian Zhang, Jun Li, Xi Wang, Yanlang Ao, Qianrong Shen
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引用次数: 0

Abstract

A fast neural network technique for retrieving vertical profiles of atmospheric temperature and water vapor from the hyperspectral infrared instrument in all-sky conditions is proposed in this study. This technique inherits from the piecewise-defined neural network (PDNN) algorithm that is presently employed operationally for the FengYun-3E Vertical Atmospheric Sounding System (VASS). A major difference from the VASS sounding is the absence of microwave observation. Thus, a new cloud-impact classification method independent of microwave radiance is developed. Additionally, the numerical weather prediction (NWP) forecast temperature can be used as the input to help obtain profile information under cloud. Validation results demonstrate that this new methodology yields higher retrieval accuracy compared to the dual-regression (DR) method currently utilized in the Geostationary Interferometric Infrared Sounder/FengYun-4B (GIIRS/FY-4B) sounding system. Improvement in retrieval accuracy can be primarily attributed to three factors: (1) the cloud-impact classification process effectively mitigates the nonlinear dependence of spectral radiance on atmospheric variables; (2) the potential influence of spectral and radiometric calibration errors of GIIRS on retrieval is minimized by employing actual GIIRS observations for network training; and (3) the incorporation of prior temperature information from forecast models. This novel approach will be used to produce the operational temperature and humidity profile products from FY4B/GIIRS.

Abstract Image

用于风云四号 B/GIIRS 高光谱辐射实时处理的神经网络温度和湿度检索技术
本研究提出了一种从高光谱红外仪器获取全天空条件下大气温度和水汽垂直剖面的快速神经网络技术。该技术继承了目前用于风云-3E 垂直大气探测系统(VASS)的片断定义神经网络(PDNN)算法。与 VASS 探空的一个主要区别是没有微波观测。因此,开发了一种独立于微波辐射的新的云影响分类方法。此外,数值天气预报(NWP)预报温度可作为输入,帮助获得云层下的剖面信息。验证结果表明,与地球静止干涉红外探测器/风云-4B(GIIRS/FY-4B)探测系统目前使用的双回归(DR)方法相比,这一新方法可获得更高的检索精度。检索精度的提高主要归因于三个因素:(1) 云影响分类过程有效地减轻了光谱辐射对大气变量的非线性依赖;(2) 通过使用实际的 GIIRS 观测数据进行网络训练,最大限度地减少了 GIIRS 的光谱和辐射校准误差对检索的潜在影响;(3) 纳入了来自预报模式的先验温度信息。这种新方法将用于制作 FY4B/GIIRS 的运行温度和湿度曲线产品。
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来源期刊
Meteorology and Atmospheric Physics
Meteorology and Atmospheric Physics 地学-气象与大气科学
CiteScore
4.00
自引率
5.00%
发文量
87
审稿时长
6-12 weeks
期刊介绍: Meteorology and Atmospheric Physics accepts original research papers for publication following the recommendations of a review panel. The emphasis lies with the following topic areas: - atmospheric dynamics and general circulation; - synoptic meteorology; - weather systems in specific regions, such as the tropics, the polar caps, the oceans; - atmospheric energetics; - numerical modeling and forecasting; - physical and chemical processes in the atmosphere, including radiation, optical effects, electricity, and atmospheric turbulence and transport processes; - mathematical and statistical techniques applied to meteorological data sets Meteorology and Atmospheric Physics discusses physical and chemical processes - in both clear and cloudy atmospheres - including radiation, optical and electrical effects, precipitation and cloud microphysics.
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