文献类型：期刊文献

英文题名：Direct Assimilation of Satellite Visible and Near-Infrared Radiances to Improve Aerosol Simulations

作者：Zhang, Chongzhao[1];Li, Qiurui[1];Li, Jun[2];Han, Wei[3,4];Zhang, Yu[5];Chen, Siqi[6];Zhang, Zhenyu[1];Dong, Yueming[1];Chang, Liang[1];Li, Jing[1,7,8]

机构：[1]Peking Univ, Sch Phys, Dept Atmospher & Ocean Sci, Beijing, Peoples R China;[2]China Meteorol Adm, Natl Satellite Meteorol Ctr, Beijing, Peoples R China;[3]China Meteorol Adm, CMA Earth Syst Modeling & Predict Ctr CEMC, Beijing, Peoples R China;[4]State Key Lab Severe Weather Meteorol Sci & Techno, Beijing, Peoples R China;[5]Guangdong Ocean Univ, Coll Ocean & Meteorol, Zhanjiang, Peoples R China;[6]China Meteorol Adm, Guangzhou Inst Trop & Marine Meteorol, GBA Acad Meteorol Res, Guangzhou, Peoples R China;[7]Peking Univ, Inst Carbon Neutral, Beijing, Peoples R China;[8]Peking Univ, Ctr Environm & Hlth, Beijing, Peoples R China

年份：2026

卷号：18

期号：9

外文期刊名：JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS

收录：SCI-EXPANDED(收录号：WOS:001884053200001)、、EI(收录号：20264121626855)、Scopus(收录号：2-s2.0-105052581536)、WOS

基金：This research was supported by the National Natural Science Foundation of China (Grant 42425503). This work is supported by the High-performance Computing Platform of Peking University. We acknowledge helpful discussions with Luyi Chen and Huiru Zhong regarding algorithm implementation and optimization. Their insights informed aspects of the technical refinement of this work.

语种：英文

外文关键词：aerosol data assimilation; shortwave radiance; AI observation operator; aerosol optical depth

外文摘要：Existing satellite-based aerosol data assimilation approaches mostly rely on assimilating retrieved aerosol products, which suffer from inconsistencies between retrieval assumptions and model representations, as well as uncertainties in the retrieved aerosol parameters. Here we develop a direct shortwave radiance assimilation system for improving aerosol simulations in the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). The system integrates AI-based observation operator, trained on 1,000,000 samples from forward radiative transfer simulations, and a three-Dimensional Variational Data Assimilation (3DVAR) assimilation module to simultaneously constrain multi-band aerosol optical depth (AOD) and surface albedo using radiance measurements from five shortwave bands of Moderate Resolution Imaging Spectroradiometer (MODIS) under clear sky. Observing System Simulation Experiment (OSSE) results demonstrate that the system can effectively disentangle aerosol and surface signals, with forward simulated radiance from the analysis field being closer to pseudo-observations than those from the background field. Experiments using MODIS radiance measurements show substantial improvements. In the February 2016 case study, assimilation reduces the domain-mean spatial AOD error relative to MODIS retrievals by 42.19%, and reduces the absolute AOD bias relative to independent AERONET observations by 53.46%-79.76% across four validation sites. The continuous assimilation experiment spanning December 2016 to February 2017 demonstrates robust long-term performance, reducing AOD errors by 52.77% overall. The assimilation scheme further improves simulated surface albedo and surface downward shortwave radiation, demonstrating the potential to better represent radiative transfer process in numerical weather prediction and climate models.