Precipitation Observation and Modelling in Urban and Coastal Areas

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

Deadline for manuscript submissions: closed (5 April 2023) | Viewed by 4149

Special Issue Editors

Faculty of Science and Technology, University of Macau, Macau, China
Interests: water-related natural hazards; hydrological process modeling; integration of remote sensing data with numerical models

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1. Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region, Ministry of Education, Chang’an University, 710064 Xi’an, China
2. School of Water and Environment, Chang’an University, 710064 Xi’an, China
Interests: urban flood; flood management; hydrological modeling; water quality analysis; statistical analysis; sustainable water resource management; ecohydrology
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Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO 80521, USA
Interests: extreme precipitation; radar hydrometeorology; remote sensing of precipitation
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Guest Editor
School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK 73072, USA
Interests: extreme precipitation; climate changes; flood modeling

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Guest Editor
Department of Civil Engineering, The University of Hong Kong (HKU), Hong Kong, China
Interests: water resources; climate change; multi-scale terrestrial hydrologic processes; urbanization; remote sensing application to hydrology; natural hazards
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Special Issue Information

Dear Colleagues,

Improved precipitation monitoring and prediction are fundamental to understanding regional and global hydrological processes, flash flood protection, emergency preparedness, etc. However, it is still difficult to acquire accurate and timely precipitation information in urban and coastal regions. To cope with these potential threats of extreme precipitation and associated hazards, deterministic or probabilistic precipitation methods have been proposed and utilized. Multi-source observation techniques have been developed, including gauge, weather radar, and satellite. Precipitation forecasting models based on deterministic or probabilistic theory and techniques have also been proved to be successful. These are the cases not only in vast inland areas but also in the urban and coastal regions. Therefore, the journal Atmosphere is dedicating this Special Issue to investigating precipitation analysis and modeling in urban and coastal areas.

We invite you to contribute to this Special Issue of Atmosphere with original research and review articles on topics including but not limited to:

  • Developing precipitation products based on gauge, weather radar, satellite, and other observation systems;
  • Comparing observed and multi-model simulated precipitation results;
  • Analyzing precipitation trends/changes based on the specific weather systems or statistics on climate scales;
  • Forecasting or nowcasting a short-term precipitation event;
  • Evaluating the impacts of the urban environment, anthropic activities, and climate changes on precipitation processes;
  • Projecting future precipitation and evaluating the impacts under different climate change scenarios;
  • Modeling the response of an urban or coastal area to the hazard events.

Dr. Liang Gao
Prof. Dr. Pingping Luo
Dr. Yingzhao Ma
Dr. Mengye Chen
Prof. Dr. Ji Chen
Guest Editors

Manuscript Submission Information

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Keywords

  • precipitation observation
  • forecasting
  • urban environment
  • coastal area
  • remote sensing
  • probabilistic and deterministic method
  • water-related hazards

Published Papers (2 papers)

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Research

18 pages, 4059 KiB  
Article
Summer Precipitation Forecast Using an Optimized Artificial Neural Network with a Genetic Algorithm for Yangtze-Huaihe River Basin, China
by Zhi-Cheng Zhang, Xin-Min Zeng, Gen Li, Bo Lu, Ming-Zhong Xiao and Bing-Zeng Wang
Atmosphere 2022, 13(6), 929; https://doi.org/10.3390/atmos13060929 - 07 Jun 2022
Cited by 5 | Viewed by 1565
Abstract
Owing to the complexity of the climate system and limitations of numerical dynamical models, machine learning based on big data has been used for climate forecasting in recent years. In this study, we attempted to use an artificial neural network (ANN) for summer [...] Read more.
Owing to the complexity of the climate system and limitations of numerical dynamical models, machine learning based on big data has been used for climate forecasting in recent years. In this study, we attempted to use an artificial neural network (ANN) for summer precipitation forecasts in the Yangtze-Huaihe River Basin (YHRB), eastern China. The major ANN employed here is the standard backpropagation neural network (BPNN), which was modified for application to the YHRB. Using the analysis data of precipitation and the predictors/factors of atmospheric circulation and sea surface temperature, we calculated the correlation coefficients between precipitation and the factors. In addition, we sorted the top six factors for precipitation forecasts. In order to obtain accurate forecasts, month (factor)-to-month (precipitation) forecast models were applied over the training and validation periods (i.e., summer months over 1979–2011 and 2012–2019, respectively). We compared the standard BPNN with the BPNN using a genetic algorithm-based backpropagation (GABP), support vector machine (SVM) and multiple linear regression (MLR) for the summer precipitation forecast after the model training period, and found that the GABP method is the best among the above methods for precipitation forecasting, with a mean absolute percentage error (MAPE) of approximately 20% for the YHRB, which is substantially lower than the BPNN, SVM and MLR values. We then selected the best summer precipitation forecast of the GABP month-to-month models by summing up monthly precipitation, in order to obtain the summer scale forecast, which presents a very successful performance in terms of evaluation measures. For example, the basin-averaged MAPE and anomaly rate reach 4.7% and 88.3%, respectively, for the YHRB, which can be a good recommendation for future operational services. It appears that sea surface temperatures (SST) in some key areas dominate the factors for the forecasts. These results indicate the potential of applying GABP to summer precipitation forecasts in the YHRB. Full article
(This article belongs to the Special Issue Precipitation Observation and Modelling in Urban and Coastal Areas)
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15 pages, 4604 KiB  
Article
Mesoscale Observational Analysis of Isolated Convection Associated with the Interaction of the Sea Breeze Front and the Gust Front in the Context of the Urban Heat Humid Island Effect
by Nan Zhang, Yan Wang and Xiaomeng Lin
Atmosphere 2022, 13(4), 603; https://doi.org/10.3390/atmos13040603 - 09 Apr 2022
Cited by 4 | Viewed by 1493
Abstract
An isolated convection was unexpectedly initiated in the evening of 1 August 2019 around the Tianjin urban region (TUR), which happened at some distance from the shear line at lower level and the preexisting convection to the South, analyzed by using ERA5 reanalysis [...] Read more.
An isolated convection was unexpectedly initiated in the evening of 1 August 2019 around the Tianjin urban region (TUR), which happened at some distance from the shear line at lower level and the preexisting convection to the South, analyzed by using ERA5 reanalysis data and observations from surface weather stations, and a S-band radar. The results show that, 42 min before the initiation of the convection, the atmospheric thermodynamic conditions around TUR were favorable for the initiation of the isolated convection, although the southerly and vertical shear of the horizontal wind at the lower level was weak. A sea-breeze front approached the TUR and continued to move West, leading to the triggering of the isolated convection in the context of the urban humid heat island (UHHI) effect. Subsequently, the gust front, which was formed between the cold pool away from the TUR and the warm and humid air of the UHHI, moved northward, approached the convection, and collided with sea breeze front, resulting in five reflectivity centers of isolated convection being merged and the convection’s development. Finally, the isolated convection split into two convections that moved away from the TUR and disappeared at 20:36 Beijing Time. The isolated convection was initiated and developed by the interaction of the sea breeze front and gust front in the context of the UHHI effect. The sea breeze front triggered the isolated convection around TUR in the context of the UHHI effect, and the gust front produced by the early convective storms to the south played a vital role in the development of the isolated convection. Full article
(This article belongs to the Special Issue Precipitation Observation and Modelling in Urban and Coastal Areas)
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