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Special Issue "Precipitation Observation and Modelling in Urban and Coastal Areas"

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

Deadline for manuscript submissions: 5 April 2023 | Viewed by 6137

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Special Issue Editors

Dr. Liang Gao

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Guest Editor

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

Prof. Dr. Pingping Luo

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Guest Editor

1. Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region, Ministry of Education, Chang'an University, Xi'an, China
2. School of Water and Environment, Chang'an University, Xi'an, China
Interests: urban flood; flood management; hydrological modeling; water quality analysis; statistical analysis; sustainable water resource management; ecohydrology
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Dr. Yingzhao Ma

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Guest Editor

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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Dr. Mengye Chen

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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

Prof. Dr. Ji Chen

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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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

Published Papers (5 papers)

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Research

Open AccessArticle

Cross-Examining Precipitation Products by Rain Gauge, Remote Sensing, and WRF Simulations over a South American Region across the Pacific Coast and Andes

Mengye Chen

Yongjie Huang

Zhi Li

Albert Johan Mamani Larico

Andres Vitaliano Perez

and

Isaac Yanqui Morales

Atmosphere 2022, 13(10), 1666; https://doi.org/10.3390/atmos13101666 - 12 Oct 2022

Viewed by 825

Abstract

Precipitation estimate is important for earth science studies and applications, and it is one of the most difficult meteorological quantities to estimate accurately. For regions such as Peru, reliable gridded precipitation products are lacking due to complex terrains and large portions of remote lands that limit the accuracy of satellite precipitation estimation and in situ measurement density. This study evaluates and cross-examines two high-resolution satellite-based precipitation products, a global rain-gauge interpolated precipitation product, and a Weather Research and Forecast (WRF) model that simulated precipitation for a ten-year period from 2010 to 2019 in the Peruvian Andes region across the Pacific coast, Andes, and in the Amazon. The precipitation estimates examined in this study are the Integrated Multi-SatellitE Retrievals for GPM (IMERG), Multi-Source Weighted-Ensemble Precipitation (MSWEP), Global Precipitation Climatology Center product (GPCC), and a 3 km grid spacing WRF-based regional climate model (RCM) simulation. The evaluation and cross-examination were performed at sub-daily (6 h), daily, and monthly time scales, and at various spatial resolutions. The results show that the WRF simulation performs as well as, if not better than, GPM IMERG in the low precipitation and dry regions but becomes inaccurate in wet regions. GPM IMERG is more suitable for higher precipitation and wet regions, and MSWEP shows a systematic overestimation over the study area. It is therefore important to choose the most suitable precipitation product based on research needs and climate condition of the study for the challenging Peruvian Andes region. Full article

(This article belongs to the Special Issue Precipitation Observation and Modelling in Urban and Coastal Areas)

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Open AccessArticle

Summer Precipitation Forecast Using an Optimized Artificial Neural Network with a Genetic Algorithm for Yangtze-Huaihe River Basin, China

and

Atmosphere 2022, 13(6), 929; https://doi.org/10.3390/atmos13060929 - 07 Jun 2022

Cited by 3 | Viewed by 1004

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 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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Open AccessArticle

Spatiotemporal Variations and Climatological Trends in Precipitation Indices in Shaanxi Province, China

and

Atmosphere 2022, 13(5), 744; https://doi.org/10.3390/atmos13050744 - 06 May 2022

Cited by 23 | Viewed by 1074

Abstract

Precipitation, as an important part of the hydrological cycle, is often related to flood and drought. In this study, we collected daily rainfall data from 21 rainfall stations in Shaanxi Province from 1961 to 2017, and calculated eight extreme climate indices. Annual and seasonal concentration indices (CI) were also calculated. The trends in the changes in precipitation were calculated using the M–K test and Sen’s slope. The results show that the precipitation correlation index and CI (concentration index) in Shaanxi Province are higher in the south and lower in the north. For the annual scale, the CI value ranges from 0.6369 to 0.6820, indicating that Shaanxi Province has a high precipitation concentration and an uneven distribution of annual precipitation. The eight extreme precipitation indices of most rainfall stations showed a downward trend during the study period, and more than half of the stations passed the 0.05 confidence interval test. Among them, the Z value of PRCPTOT (annual total precipitation in wet days) at Huashan station reached −6.5270. The lowest slope of PRCPTOT reached −14.3395. This shows that annual rainfall in Shaanxi Province has been decreasing in recent decades. These findings could be used to make decisions about water resources and drought risk management in Shaanxi Province, China. Full article

(This article belongs to the Special Issue Precipitation Observation and Modelling in Urban and Coastal Areas)

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Open AccessArticle

A Quantitative Analysis of the Influence of Temperature Change on the Extreme Precipitation

and

Atmosphere 2022, 13(4), 612; https://doi.org/10.3390/atmos13040612 - 11 Apr 2022

Cited by 20 | Viewed by 1698

Abstract

As an essential part of the hydrological cycle, precipitation is usually associated with floods and droughts and is increasingly being paid attention to in the context of global warming. Analyzing the change trends and correlation of temperature and extreme precipitation indicators can effectively identify natural disasters. This study aimed to detect the correlation and change trends of temperature and extreme precipitation indicators in Inner Mongolia from 1960 to 2019. Panel vector autoregression (PVAR) models based on Stata software were used to detect the correlation between temperature and extreme precipitation indicators at 35 climatological stations throughout Inner Mongolia. The temperature and extreme precipitation indicator trends were analyzed using the Mann–Kendall test and Sen’s slope method. The spatial distribution characteristics of the annual precipitation and rainfall intensity were more significant in the southeast and more minor in the northwest, while an increase in the annual wet days was noticeable to the northeast. The Granger cause tests of the temperature and the extreme precipitation indicators showed a correlation between each indicator and temperature at the significance level of 1%. The temperature positively correlated with only the rainfall intensity while negatively correlating with the remaining indicators. There is no doubt that trend analysis showed significant increasing trends in rainfall intensity at all stations, which means increased risk in extreme precipitation events. By contrast, the annual precipitation and annual wet days showed significant decreasing trends, which means that the precipitation is concentrated, and it is easier to form extreme precipitation events. The study can provide a basis for decision-making in water resources and drought/flood risk management in Inner Mongolia, China. Full article

(This article belongs to the Special Issue Precipitation Observation and Modelling in Urban and Coastal Areas)

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Open AccessArticle

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

Nan Zhang

Yan Wang

and

Xiaomeng Lin

Atmosphere 2022, 13(4), 603; https://doi.org/10.3390/atmos13040603 - 09 Apr 2022

Cited by 1 | Viewed by 796

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 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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