предыдущий|следующий

Review of temperature profile inversion of satellite-borne infrared hyperspectral sensors

  • role： First author第一作者
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

    University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：2025423746@qq.com
  • Introduction：曹西凤，1996年生，女,硕士研究生，研究方向为定量遥感的理论和应用。E-mail:2025423746@qq.com
CAO Xifeng12,  
  • role： Corresponding author通信作者
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

  • Email：Lixy01@radi.ac.cn
  • Introduction：李小英，1975生,女，副研究员，研究方向为太赫兹波段临边探测原理与大气成分反演算法。E-mail:Lixy01@radi.ac.cn
LI Xiaoying1*,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

    University of Chinese Academy of Sciences, Beijing 100049, China

LUO Qi12,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

    University of Chinese Academy of Sciences, Beijing 100049, China

LIU Shuanghui12,  
  • Affiliation：

    Research Institute of Chemical Defense, Academy of Military Sciences, Beijing 102205, China

LI Peng3,  
  • Affiliation：

    Research Institute of Chemical Defense, Academy of Military Sciences, Beijing 102205, China

LIU Xin3

реферат

Global warming is a hot topic in recent years as it can lead to temperature increasing, more frequent extreme weather, etc. Temperature is an important parameter to characterize the thermodynamic state of the atmosphere. The distribution of temperature affects the radiation flux of long-wave and short-wave, which in turn plays a significant role in the balance of global energy radiation budget. Therefore, understanding the spatial and temporal distribution of atmospheric temperature and its long-term variation comprehensively are essential for our research on weather forecast and climate change research.Presently, many ground-based and space borne sensors have been developed for temperature detection. Space borne sensors observe temperature by nadir viewing, occultation detection, or limb sounding. Space borne sensors have a high spatial and temporal resolution, which can provide sufficient data for scientific research. In this paper, seven foreign hyperspectral sensors (IMG, AIRS, IASI, HALOE, HALOE, TES, MIPAS, ACE-FTS) and domestic sensors (the series of FY and GF) are introduced in terms of parameters, performance, and application. These sensors acquire the information of atmospheric parameters through the radiation in infrared band. Occultation and limb observation are less affected by the underlying surface and have high vertical resolution and sensitivity, such as HALOE, MIPAS, ACE-FTS, TES, etc. So far, the accuracy of temperature products of nadir observation (AIRS and IASI) is less than 1K with a vertical direction of 1 km, and the accuracy of temperature products in the troposphere by TES limb observation can reach 0.5 K. All of them meet the requirement of Numerical Weather Prediction and can be used widely in weather forecasting and climate change research.The accuracy of temperature profiles of the Feng Yun series is comparable to that of IASI, but the spatial and temporal resolution are needed to be improved. The radiation transfer is the basis for trace gas inversion. Firstly, we introduced the radiation transfer equations of nadir viewing, occultation detection, or limb sounding. The radiation transfer process of limb observation is similar to that of occultation detection, in which line-of-sight reaching a down-looking sensor is entirely above the ground. Then, three retrieval methods (statistical regression, physical inversion, and artificial neural network inversion) of temperature profile are described in terms of principles, characteristics, and development history. The advantages and disadvantages of them are also compared. The statistical regression method is simple and efficient, but the accuracy is relatively lower, which is usually used as the a priori profiles for the physical algorithm. The accuracy of physical inversion is improved obviously, but the retrieval process of is complex and the a priori profiles are needed in physical inversion. The artificial neural network method can acquire the temperature profile accurately and efficiently, but a variety of samples are needed for training. Meanwhile, the key problems that the influence of the cloud, aerosol, and surface reflectivity on temperature retrieval are described, and the possible solutions are then given, respectively. The generation and propagation of error (smoothness, model parameter, and measurement error) in temperature retrieval are summarized. Finally, the problems existing in atmospheric temperature inversion are proposed.

ключеви́че слова́

satellite-borne infrared hyperspectral sensor;atmospheric temperature profile;radiation transmission;key issues;error analysis

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