A Multi-Domain Compression Radiative Transfer Model for the Fengyun-4 Geosynchronous Interferometric Infrared Sounder (GIIRS)

Abstract

Forward radiative transfer (RT) models are essential for atmospheric applications such as remote sensing and weather and climate models, where computational efficiency becomes equally as important as accuracy for high-resolution hyperspectral measurements that need rigorous RT simulations for thousands of channels. This study introduces a fast and accurate RT model for the hyperspectral infrared (HIR) sounder based on principal component analysis (PCA) or machine learning (i.e., neural network, NN). The Geosynchronous Interferometric Infrared Sounder (GIIRS), the first HIR sounder onboard the geostationary Fengyun-4 satellites, is considered to be a candidate example for model development and validation. Our method uses either PCA or NN (PCA/NN) twice for the atmospheric transmittance and radiance, respectively, to reduce the number of independent but similar simulations to accelerate RT simulations; thereby, it is referred to as a multi-domain compression model. The first PCA/NN gives monochromatic gas transmittance in both spectral and atmospheric pressure domains for each gas independently. The second PCA/NN is performed in the traditional spectral radiance domain. Meanwhile, a new method is introduced to choose representative variables for the PCA/NN scheme developments. The model is three orders of magnitude faster than the standard line-by-line-based simulations with averaged brightness temperature difference (BTD) less than 0.1 K, and the compressions based on PCA or NN methods result in comparable efficiency and accuracy. Our fast model not only avoids an excessively complicated transmittance scheme by using PCA/NN but is also highly flexible for hyperspectral instruments with similar spectral ranges simply by updating the corresponding spectral response functions.

摘 要

由于吸收系数的剧烈变化,高光谱大气吸收的计算和存储量不容忽视。因此,本文基于多维度的信息冗余,研发了一种快速且准确的基于主成分分析 (Principal Component Analysis,PCA)和机器学习(即神经网络,Neural Network,NN) 方法的晴空高光谱快速辐射传输计算模型。地球同步干涉红外探测仪 (Geosynchronous Interferometric Infrared Sounder,GIIRS)是搭载于风云四号静止卫星的第一个红外高光谱探测仪,被用于验证本文所研发模型的计算精度和效率。本文所研发的模型分别在大气透过率维度和辐射维度应用了PCA和NN算法,进一步压缩信息,减少了单色的精确模拟数量,从而加速模型计算。本模型在GIIRS上的验证结果显示:与精确的逐线模型相比,本模型的平均亮温差小于 0.1 K,且计算效率提升了约三个数量级。同时这两种方法应用在不同大气条件时虽各有优势,但是二者计算精度和效率均较为接近,可以视具体情况选择更为适合的方法。本论文所研发的快速模型还避免了额外的复杂透过率方案的使用,对于具有类似光谱范围的高光谱仪器,仅需改变模型中使用的仪器对应光谱响应函数即可,具有高度的灵活性。除了最终计算得到的仪器通道辐射量和亮温,本模型还可提供中间输出的高光谱大气透过率和辐射量数据集,可直接或作为其他模型子模块使用,适用的科研和业务场景也更为丰富。