Journal Article10.1016/J.ATMOSENV.2020.118022
Machine learning based bias correction for numerical chemical transport models
25
TL;DR: An approach based on machine learning is applied to predict model bias in the CTM and it is then combined with the C TM for formulating a hybrid forecast system, the first time that machine learning methods are used in this way.
read more
About: This article is published in Atmospheric Environment. The article was published on 01 Mar 2021. The article focuses on the topics: Forecast skill & Mean absolute percentage error.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Review of urban computing in air quality management as smart city service: An integrated IoT, AI, and cloud technology perspective
TL;DR: In this paper, the authors provide a perspective regarding the opportunity available in addressing the urban air quality management (UAQM) issues using smart city framework in the context of "urban computing".
136
Development of a regional feature selection-based machine learning system (RFSML v1.0) for air pollution forecasting over China
TL;DR: A regional feature selection-based machine learning (RFSML) system was developed, which is capable of predicting air quality in the short term with high accuracy at the national scale and provides insights into the difference in interpretability among machine learning models.
SmartAirQ: A Big Data Governance Framework for Urban Air Quality Management in Smart Cities
Akshara Kaginalkar,Shamita Kumar,Prashant Gargava,Neelesh Kharkar,Dev Niyogi +4 more
TL;DR: A conceptual urban computing framework “SmartAirQ” for UAQM is designed that has integrated science cloud and urban services aiding in translating scientific data into operations and is a step toward collaborative, data-driven, and sustainable smart cities.
12
Research Progress, Challenges and Prospects of PM2.5 Concentration Estimation using Satellite Data
TL;DR: In this article , the authors reviewed the PM2.5 estimation process based on satellite AOD data in terms of data sources, estimation models (i.e., statistical regression, chemical transport models, machine learning and combinatorial analysis).
11
Increasing heatwave with associated population and GDP exposure in North China
TL;DR: In this article , the authors grouped 27 CMIP6 models into three ensembles based on the simulated performance of heatwaves in North China during present-day (1995-2014), and future changes in the duration and intensity of heatwave were projected under SSP1-2.6, SSP2-4.5 and SSP5-8.5.
10
References
Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics
TL;DR: In this article, a new sequential data assimilation method is proposed based on Monte Carlo methods, a better alternative than solving the traditional and computationally extremely demanding approximate error covariance equation used in the extended Kalman filter.
Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences
M.W. Gardner,Stephen Dorling +1 more
TL;DR: This paper presents a general introduction and discussion of recent applications of the multilayer perceptron, one type of artificial neural network, in the atmospheric sciences.
3.2K
•Book
Atmospheric Modeling, Data Assimilation and Predictability
Eugenia Kalnay
- 01 Nov 2002
TL;DR: A comprehensive text and reference work on numerical weather prediction, first published in 2002, covers not only methods for numerical modeling, but also the important related areas of data assimilation and predictability.
Global modeling of tropospheric chemistry with assimilated meteorology : Model description and evaluation
Isabelle Bey,Daniel J. Jacob,Robert M. Yantosca,Jennifer A. Logan,B. D. Field,Arlene M. Fiore,Qinbin Li,Honguy Y. Liu,Loretta J. Mickley,Martin G. Schultz +9 more
TL;DR: The GEOS-CHEM model as mentioned in this paper is a 3D model of tropospheric chemistry driven by assimilated meteorological observations from the Goddard Earth Observing System (GEOS) of the NASA Data Assimilation Office (DAO).
Chemistry and Physiology of Los Angeles Smog
TL;DR: In this paper, the photochemical action of nitrogen oxides oxidizes the hydrocarbons and thereby forms ozone, responsible for rubber cracking, giving eye irritation and crop damage resembling closely that observed on smog days.
817