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CMAQ的伴随模型

The adjoint of CMAQ.

作者信息

Hakami Amir, Henze Daven K, Seinfeld John H, Singh Kumaresh, Sandu Adrian, Kim Soontae, Byun Daewon, Li Qinbin

机构信息

Department of Chemical Engineering, California Institute of Technology, 1200 E. California Boulevard, Pasadena, California 91125, USA.

出版信息

Environ Sci Technol. 2007 Nov 15;41(22):7807-17. doi: 10.1021/es070944p.

Abstract

An adjoint model for the internationally used Community Multiscale Air Quality (CMAQ) modeling platform of the U.S. EPA is developed. The adjoint version for CMAQ (CMAQ-ADJ) provides the user community with forward (decoupled direct method or DDM) and backward (adjoint) sensitivity analysis capabilities. Current implementation is for gas-phase processes. Discrete adjoints are implemented for all processes with the exception of horizontal advection, for which, because of inherent discontinuities in the advection scheme, the continuous approach is superior. The adjoint of chemistry is constructed by interfacing CMAQ with the kinetic pre-processor, which provides for increased flexibility in the choice of chemical solver and facilitates the implementation of new chemical mechanisms. The adjoint implementation is evaluated both on a process-by-process basis and for the full model. In general, adjoint results show good agreement with brute-force and DDM sensitivities. As expected for a continuous adjoint implementation in a nonlinear scheme, the agreement is not perfect for horizontal transport. Sensitivities of various air quality, public health, and environmental metrics with respect to emissions are calculated using the adjoint method. In order to show applicability to regional climate studies, as an example, the sensitivities of these metrics with respect to local temperatures are calculated.

摘要

美国环境保护局(EPA)开发了一种用于国际上广泛使用的社区多尺度空气质量(CMAQ)建模平台的伴随模型。CMAQ的伴随版本(CMAQ-ADJ)为用户群体提供了正向(解耦直接法或DDM)和反向(伴随)敏感性分析能力。目前的实现是针对气相过程的。除水平平流外,所有过程均采用离散伴随方法实现,由于平流方案中存在固有的不连续性,连续方法在水平平流方面更具优势。化学过程的伴随通过将CMAQ与动力学预处理器连接来构建,这在化学求解器的选择上提供了更大的灵活性,并便于新化学机制的实施。伴随实现分别在逐个过程的基础上以及对整个模型进行了评估。总体而言,伴随结果与蛮力法和DDM敏感性结果显示出良好的一致性。正如在非线性方案中连续伴随实现所预期的那样,对于水平传输,一致性并不完美。使用伴随方法计算了各种空气质量、公共卫生和环境指标相对于排放的敏感性。为了展示其在区域气候研究中的适用性,作为一个例子,计算了这些指标相对于当地温度的敏感性。

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