CHEEREIO 1.0: a versatile and user-friendly ensemble-based chemical data assimilation and emissions inversion platform for the GEOS-Chem chemical transport model

IF 4 3区地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY

Geoscientific Model Development Pub Date : 2023-08-24 DOI:10.5194/gmd-16-4793-2023

D. Pendergrass, D. Jacob, H. Nesser, D. Varon, M. Sulprizio, K. Miyazaki, K. Bowman

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引用次数: 1

Abstract

Abstract. We present a versatile, powerful, and user-friendly chemical data assimilation toolkit for simultaneously optimizing emissions and concentrations of chemical species based on atmospheric observations from satellites or suborbital platforms. The CHemistry and Emissions REanalysis Interface with Observations (CHEEREIO) exploits the GEOS-Chem chemical transport model and a localized ensemble transform Kalman filter algorithm (LETKF) to determine the Bayesian optimal (posterior) emissions and/or concentrations of a set of species based on observations and prior information using an easy-to-modify configuration file with minimal changes to the GEOS-Chem or LETKF code base. The LETKF algorithm readily allows for nonlinear chemistry and produces flow-dependent posterior error covariances from the ensemble simulation spread. The object-oriented Python-based design of CHEEREIO allows users to easily add new observation operators such as for satellites. CHEEREIO takes advantage of the Harmonized Emissions Component (HEMCO) modular structure of input data management in GEOS-Chem to update emissions from the assimilation process independently from the GEOS-Chem code. It can seamlessly support GEOS-Chem version updates and is adaptable to other chemical transport models with similar modular input data structure. A post-processing suite combines ensemble output into consolidated NetCDF files and supports a wide variety of diagnostic data and visualizations. We demonstrate CHEEREIO's capabilities with an out-of-the-box application, assimilating global methane emissions and concentrations at weekly temporal resolution and 2∘ × 2.5∘ spatial resolution for 2019 using TROPOspheric Monitoring Instrument (TROPOMI) satellite observations. CHEEREIO achieves a 50-fold improvement in computational performance compared to the equivalent analytical inversion of TROPOMI observations.

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CHEEREIO 1.0：一个通用且用户友好的基于集合的化学数据同化和排放反演平台，用于GEOS化学传输模型

摘要我们提出了一个多功能、功能强大、用户友好的化学数据同化工具包，用于同时优化基于卫星或亚轨道平台大气观测的化学物质的排放和浓度。化学和排放再分析接口与观测(CHEEREIO)利用GEOS-Chem化学传输模型和局部集合变换卡尔曼滤波算法(LETKF)来确定贝叶斯最优(后验)排放和/或浓度的一组物种基于观测和先验信息，使用一个易于修改的配置文件与最小的更改GEOS-Chem或LETKF代码库。LETKF算法很容易允许非线性化学，并从集合模拟扩展产生流相关的后检误差协方差。cheereio基于python的面向对象设计允许用户轻松添加新的观测操作符，例如卫星。CHEEREIO利用GEOS-Chem中输入数据管理的协调排放组件(HEMCO)模块化结构，独立于GEOS-Chem代码更新同化过程中的排放。它可以无缝支持GEOS-Chem版本更新，并适用于具有类似模块化输入数据结构的其他化学运输模型。后处理套件将集成输出合并到统一的NetCDF文件中，并支持各种诊断数据和可视化。我们通过一个开箱即用的应用程序展示了echeereio的能力，利用对流层监测仪器(TROPOMI)卫星观测，以周时间分辨率和2°× 2.5°空间分辨率吸收2019年全球甲烷排放和浓度。与TROPOMI观测的等效解析反演相比，CHEEREIO的计算性能提高了50倍。

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来源期刊

Geoscientific Model Development GEOSCIENCES, MULTIDISCIPLINARY-

CiteScore

8.60

自引率

9.80%

发文量

352

审稿时长

6-12 weeks

期刊介绍： Geoscientific Model Development (GMD) is an international scientific journal dedicated to the publication and public discussion of the description, development, and evaluation of numerical models of the Earth system and its components. The following manuscript types can be considered for peer-reviewed publication: * geoscientific model descriptions, from statistical models to box models to GCMs; * development and technical papers, describing developments such as new parameterizations or technical aspects of running models such as the reproducibility of results; * new methods for assessment of models, including work on developing new metrics for assessing model performance and novel ways of comparing model results with observational data; * papers describing new standard experiments for assessing model performance or novel ways of comparing model results with observational data; * model experiment descriptions, including experimental details and project protocols; * full evaluations of previously published models.