CMAQ-CNN: A new-generation of post-processing techniques for chemical transport models using deep neural networks-Reference-Cited by-同舟云学术

CMAQ-CNN: A new-generation of post-processing techniques for chemical transport models using deep neural networks

Published:2022-03 Issue: Volume:273 Page:118961
ISSN:1352-2310
Container-title:Atmospheric Environment
language:en
Short-container-title:Atmospheric Environment

Author:

Sayeed Alqamah,Eslami Ebrahim,Lops Yannic^ORCID,Choi Yunsoo

Funder

University of Houston

Publisher

Elsevier BV

Subject

Atmospheric Science,General Environmental Science

Reference50 articles.

1. Tensorflow: a system for large-scale machine learning;Abadi;OSDI,2016

2. Description and evaluation of the Community Multiscale Air Quality (CMAQ) modeling system version 5.1;Appel;Geosci. Model Dev. (GMD),2017

3. Long-term wind speed and power forecasting using local recurrent neural network models;Barbounis;IEEE Trans. Energy Convers.,2006

4. Review of the Governing Equations, Computational Algorithms, and Other Components of the Models-3 Community Multiscale Air Quality (CMAQ) Modeling System;Byun,2006

5. Evaluation of surface sensible weather forecasts by the WRF and the Eta models over the western United States;Cheng;Weather Forecast.,2005

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