Multi-source Meteorological Data Fusion and Assimilation Methods

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Meteorology".

Deadline for manuscript submissions: 27 May 2024 | Viewed by 1539

Special Issue Editor

Earth System Modeling and Prediction Centre, China Meteorological Administration, Beijing 100081, China
Interests: precipitation data fusion; land data assimilation; land–atmosphere coupled data assimilation; short-term climate prediction

Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue dedicated to exploring the latest advancements in the field of multi-source meteorological data fusion and assimilation. This rapidly evolving research area focuses on integrating diverse data from multiple sources to enhance our understanding and improve the accuracy of weather, hydrological, and ocean forecasting and analysis.

The objective of this Special Issue is to showcase cutting-edge research, methodologies, and practical applications related to the fusion and assimilation of meteorological data from various sources. We encourage researchers, scientists, and practitioners in the field of meteorology to contribute their original work to this Special Issue, thereby fostering interdisciplinary collaboration and driving advancements in this crucial domain.

Topics of interest include, but are not limited to:

  • Innovative data fusion techniques for integrating meteorological observations from different sources;
  • Advanced assimilation methods for incorporating data into numerical weather prediction models;
  • Applications of multi-source data fusion and assimilation in improving weather forecasting accuracy and extreme weather event prediction;
  • Evaluation and validation of data fusion and assimilation techniques using observational and model data;
  • Challenges and future directions in multi-source meteorological data fusion and assimilation.

By bringing together diverse perspectives and expertise, we aim to provide a comprehensive overview of the state-of-the-art in this field and inspire new research avenues. In this context, we are calling for submissions related but not limited to the above topics. We invite researchers to join us in this endeavor to present novel findings, theoretical developments, and practical insights that push the boundaries of multi-source meteorological data fusion and assimilation. We look forward to receiving your contributions and working together to advance the field of multi-source meteorological data fusion and assimilation.

Dr. Suping Nie
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

  • multi-source data fusion
  • assimilation methods
  • meteorological observations
  • model–data integration
  • weather forecasting

Published Papers (1 paper)

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Research

16 pages, 7999 KiB  
Article
The Effects of Planetary Boundary Layer Features on Air Pollution Based on ERA5 Data in East China
by Xueliang Deng, Jian Chen, Rui Dai, Zhenfang Zhai, Dongyan He, Liang Zhao, Xiaolong Jin and Jiping Zhang
Atmosphere 2023, 14(8), 1273; https://doi.org/10.3390/atmos14081273 - 11 Aug 2023
Viewed by 1205
Abstract
The planetary boundary layer (PBL) structure and its evolution can significantly affect air pollution. Here, the PBL’s characteristics and their association with air pollution were analyzed in Hefei, east China, using ERA5 reanalysis data, weather observations and air pollutant measurements from 2016 to [...] Read more.
The planetary boundary layer (PBL) structure and its evolution can significantly affect air pollution. Here, the PBL’s characteristics and their association with air pollution were analyzed in Hefei, east China, using ERA5 reanalysis data, weather observations and air pollutant measurements from 2016 to 2021. In the near-surface level, air pollution was directly influenced by ground meteorological conditions, and high PM2.5 was normally related to weak wind speed, northwest wind anomalies, low temperature and high relative humidity. Moreover, in the trajectory analysis, air masses from the north and the northwest with short length played an important role in the high PM2.5 with pollutant transport within the PBL. Furthermore, high PM2.5 showed a tight dependence on PBL stratification. There was high temperature and relative humidity and low wind speed and PBL height within all PBL altitudes in the polluted condition. Notably, vertical wind shear (VWS) and temperature gradient tended to be much weaker below 900 hPa, which created a deeply stable stratification that acted as a cap to upward-moving air. Such a PBL structure facilitated more stable stratification and enhanced the generation of air pollution. Finally, the stable stratification in the PBL was related to the special synoptic configuration for the high PM2.5 conditions, which included the block situation at the high level, the southerly wind anomalies at the middle level and the wild range of the uniform pressure field at the near-ground level. Therefore, air pollutant concentrations were regulated by ground factors, PBL structure and the synoptic situation. Our results provide a precise understanding of the role of PBL features in air pollution, which contributes to improving the assimilation method of the atmospheric chemistry model in east China. Full article
(This article belongs to the Special Issue Multi-source Meteorological Data Fusion and Assimilation Methods)
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