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Atmosphere
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Improving Extreme Precipitation Simulation
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Improving Extreme Precipitation Simulation

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: closed (25 March 2024) | Viewed by 11955

Special Issue Editors

Dr. Qiguang Wang

E-Mail Website
Guest Editor
China Meteorological Administration Training Center, China Meteorological Administration, Beijing 100081, China
Interests: artificial Intelligence, numerical modeling；extended-range forecast；nonlinear dynamics；extreme events；complex network
Special Issues, Collections and Topics in MDPI journals
Dr. Fengxue Qiao

E-Mail Website
Guest Editor
School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Interests: climate modeling; numerical simulation; cumulus parameterization; extreme precipitation
Dr. Chao Sun

E-Mail Website
Guest Editor
Department of Atmospheric & Oceanic Science, University of Maryland, College Park, MD 20742, USA
Interests: machine learning; model evaluation; cumulus parameterization; extreme precipitation

Special Issue Information

Dear Colleagues,

Extreme precipitation profoundly impacts global society. Each year, floods induced by extreme precipitation cause thousands of deaths globally, with billions of dollars in damages. Governments rely on both improved observations and more accurate forecasts to minimize the damage from these disasters. In recent decades, remote sensing has substantially improved our ability to monitor extreme precipitation events, while improved understanding of the physical mechanisms underlying extreme precipitation have greatly improved modeling capabilities. The numerical modeling community has applied these advances to significantly improve extreme precipitation simulation. However, persistent challenges remain, not only slowing down our further understanding of the mechanism of extreme precipitation processes but also hindering the production of highly reliable extreme precipitation event forecasting. Ultimately, they reduced the certainty and accuracy in the future projection of how extreme precipitation evolves in the future.

In recognition of these urgent needs to improve model skill in capturing extreme precipitation, the open-access journal Atmosphere is hosting a Special Issue to showcase the most recent findings related to extreme precipitation simulation, model improvement, parameterization scheme development, and process understanding. Given the recent research showing that global warming significantly exacerbates extreme precipitation events, this Special Issue is also an appropriate venue for papers that deal with extreme precipitation projection under future climate conditions. We also welcome any studies that apply the latest machine learning techniques to improve extreme precipitation simulation. Overall, this Special Issue aims to highlight the most recent developments, techniques, and physical understandings, as well as new evidence from observations. We also encourage relevant studies assessing the societal effects of extreme precipitation based on numerical model results.

Original results from model studies, model evaluation, model development, surveys, and review papers related to extreme precipitation are all welcome contributions. Authors are encouraged to include a section touching on future issues, opportunities, and/or concerns related to their topics, on the 5-, 10-, and 20-year horizons.

Dr. Qiguang Wang
Dr. Fengxue Qiao
Dr. Chao Sun
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 2400 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

  • extreme precipitation
  • model evaluation
  • model development
  • numerical simulation
  • cumulus parameterization
  • machine learning

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Published Papers (6 papers)

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Research

18 pages, 3597 KiB  
Article
Application of Machine Learning Techniques to Improve Multi-Radar Mosaic Precipitation Estimates in Shanghai
by Rui Wang, Hai Chu, Qiyang Liu, Bo Chen, Xin Zhang, Xuliang Fan, Junjing Wu, Kang Xu, Fulin Jiang and Lei Chen
Atmosphere 2023, 14(9), 1364; https://doi.org/10.3390/atmos14091364 - 29 Aug 2023
Cited by 3 | Viewed by 1135
Abstract
In this study, we applied an explainable machine learning technique based on the LightGBM method, a category of gradient boosting decision tree algorithm, to conduct a quantitative radar precipitation estimation and move to understand the underlying reasons for excellent estimations. By introducing 3D [...] Read more.
In this study, we applied an explainable machine learning technique based on the LightGBM method, a category of gradient boosting decision tree algorithm, to conduct a quantitative radar precipitation estimation and move to understand the underlying reasons for excellent estimations. By introducing 3D grid radar reflectivity data into the LightGBM algorithm, we constructed three LightGBM models, including 2D and 3D LightGBM models. Ten groups of experiments were carried out to compare the performances of the LightGBM models with traditional Z–R relationship methods. To further assess the performances of the LightGBM models, rainfall events with 11,483 total samples during August-September of 2022 were used for statistical analysis, and two heavy rainfall events were specifically chosen for the spatial distribution evaluation. The results from both the statistical analysis and spatial distribution demonstrate that the performance of the LightGBM 3D model with nine points is the best method for quantitative precipitation estimation in this study. Through analyzing the explainability of the LightGBM models from Shapley additive explanations (SHAP) regression values, it can be inferred that the superior performance of the LightGBM 3D model is mainly attributed to its consideration of the rain gauge station attributes, diurnal variation characteristics, and the influence of spatial offset. Full article
(This article belongs to the Special Issue Improving Extreme Precipitation Simulation)
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18 pages, 11407 KiB  
Article
Sichuan Rainfall Prediction Using an Analog Ensemble
by Pengyou Lai, Jingtao Yang, Lexi Liu, Yu Zhang, Zhaoxuan Sun, Zhefan Huang, Duanzhou Shao and Linbin He
Atmosphere 2023, 14(8), 1223; https://doi.org/10.3390/atmos14081223 - 29 Jul 2023
Viewed by 1267
Abstract
This study aimed to address the significant bias in 0–44-day precipitation forecasts under numerical weather conditions. To achieve this, we utilized observational data obtained from 156 surface stations in the Sichuan region and reanalysis grid data from the National Centers for Environmental Prediction [...] Read more.
This study aimed to address the significant bias in 0–44-day precipitation forecasts under numerical weather conditions. To achieve this, we utilized observational data obtained from 156 surface stations in the Sichuan region and reanalysis grid data from the National Centers for Environmental Prediction Climate Forecast System Model version 2. Statistical analysis of the spatiotemporal characteristics of precipitation in Sichuan was conducted, followed by a correction experiment based on the Analog Ensemble algorithm for 0–44-day precipitation forecasts for different seasons in the Sichuan region. The results show that, in terms of spatial distribution, the precipitation amounts and precipitation days in Sichuan Province gradually decreased from east to west. Temporally, the highest number of precipitation days occurred in autumn, while the maximum precipitation amount was observed in summer. The Analog Ensemble algorithm effectively reduced the error in the model forecast results for different seasons in the Sichuan region. However, the correction effectiveness varied seasonally, primarily because of the differing performance of the AnEn method in relation to precipitation events of various magnitudes. Notably, the correction effect was the poorest for heavy-rain forecasts. In addition, the degree of improvement of the Analog Ensemble algorithm varied for different initial forecast times and forecast lead times. As the forecast lead time increased, the correction effect gradually weakened. Full article
(This article belongs to the Special Issue Improving Extreme Precipitation Simulation)
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28 pages, 14292 KiB  
Article
Effects of Microphysics Parameterizations on Forecasting a Severe Hailstorm of 30 April 2021 in Eastern China
by Fulin Jiang, Bo Chen, Fengxue Qiao, Rui Wang, Chaoshi Wei and Qiyang Liu
Atmosphere 2023, 14(3), 526; https://doi.org/10.3390/atmos14030526 - 9 Mar 2023
Cited by 1 | Viewed by 1902
Abstract
On the evening of 30 April 2021, a severe hailstorm swept across eastern China, causing catastrophic gale and damaging hailstones. This hailstorm event was directly caused by two mesoscale convective systems associated with strong squall lines, with mid-level cold advection from the northeast [...] Read more.
On the evening of 30 April 2021, a severe hailstorm swept across eastern China, causing catastrophic gale and damaging hailstones. This hailstorm event was directly caused by two mesoscale convective systems associated with strong squall lines, with mid-level cold advection from the northeast cold vortex, and strong low-level convergence associated with the low-level vortex and wind shear line. Double nesting of the high-resolution weather research and forecasting model (9–1 km) is utilized to simulate this hailstorm with five microphysics schemes. The radar-based maximum estimated size of hail (MESH) algorithm, differential reflectivity and fractions skill scores were used to quantitatively evaluate the precision. All schemes basically captured the two squall lines that swept through eastern China, although they appeared one or two hours earlier than observation. Particularly, Goddard and Thompson performed better in the MESH swath and fractions skill scores among the five different schemes. However, Thompson most realistically captured the reflectivity pattern, intensity and vertical structure of mesoscale convective systems. Its high-reflectivity column corresponded to the maximum center of the hail mixing ratio within the updraft region, which is consistent with the characteristics of a pulse-type hailstorm in its mature phase. Full article
(This article belongs to the Special Issue Improving Extreme Precipitation Simulation)
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15 pages, 5880 KiB  
Article
Identification of Patterns and Relationships of Jet Stream Position with Flood-Prone Precipitation in Iran during 2006–2019
by Iman Rousta, Abazar Esmaeili Mahmoudabadi, Parisa Amini, Armin Nikkhah, Haraldur Olafsson, Jaromir Krzyszczak and Piotr Baranowski
Atmosphere 2023, 14(2), 351; https://doi.org/10.3390/atmos14020351 - 10 Feb 2023
Viewed by 2361
Abstract
Jet streams are atmospheric phenomena that operate on a synoptic scale and can intensify the descending/ascending conditions of the air at the lower levels of the atmosphere. This study aimed to identify the patterns and location of the jet stream in southwest Asia [...] Read more.
Jet streams are atmospheric phenomena that operate on a synoptic scale and can intensify the descending/ascending conditions of the air at the lower levels of the atmosphere. This study aimed to identify the patterns and location of the jet stream in southwest Asia during the days of widespread rainfall in Iran based on two criteria: “highest frequency of stations involved” and “maximum cumulative amount on the day of peak rainfall”. For this purpose, the daily precipitation data for 42 synoptic stations in Iran during the period 2006–2019 from the Meteorological Organization of Iran, the daily data at 500 hPa Geopotential Height (HGT), and U and V wind components at 500 and 300 hPa from NCEP/NCAR were gathered. Synoptic patterns were obtained based on daily precipitation data, daily maps at HGT 500 hPa, and U and V wind components at 500 and 300 hPa. The analysis of patterns showed that the position of precipitation cores is associated with the position and extension of jet stream centers at 300 hPa in winter, spring, and autumn. The main position of jet stream cores during flood-causing rainfall at 300 hPa was over the northern part of Saudi Arabia, the Mesopotamia basin, and southern Iran. This position seems to have provided the conditions for the convergence of the earth’s surface and the divergence of the atmosphere for the easy passage of moisture from the Red Sea, Aden Sea, and the Persian Gulf, and in the second rank, the Mediterranean Sea and the Arabian Sea. Full article
(This article belongs to the Special Issue Improving Extreme Precipitation Simulation)
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13 pages, 20123 KiB  
Article
Analogue Ensemble Averaging Method for Bias Correction of 2-m Temperature of the Medium-Range Forecasts in China
by Yingying Hu, Qiguang Wang and Xueshun Shen
Atmosphere 2022, 13(12), 2097; https://doi.org/10.3390/atmos13122097 - 13 Dec 2022
Viewed by 1766
Abstract
The 2-m temperature is one of the important meteorological elements, and improving the accuracy of medium- and long-term forecasts of the 2-m temperature is important. The similarity forecasting method is widely used as a calibration technique in the statistical postprocessing of numerical weather [...] Read more.
The 2-m temperature is one of the important meteorological elements, and improving the accuracy of medium- and long-term forecasts of the 2-m temperature is important. The similarity forecasting method is widely used as a calibration technique in the statistical postprocessing of numerical weather prediction (NWP). In this study, the analogue ensemble averaging method is used to correct the deterministic forecast of the 2-m temperature with a forecast lead time from 180 h to 348 h using the CMA-GEPS model. The bias, mean absolute error (MAE), and root mean square error (RMSE) are used as the evaluation metrics. In comparison with NWP, the systematic error of the model for 2-m temperature is effectively reduced during each forecast period when using the analogue ensemble averaging method. In addition, the differences in forecast errors between regions are reduced, and the accuracy of 2-m temperature forecasts over complex terrain, especially in Southwest China, Northwest China, and North China, is improved using this method. In the future, there is certainly potential to apply the analogue ensemble averaging method to the bias correction of medium- and long-term forecasts of more meteorological elements. Full article
(This article belongs to the Special Issue Improving Extreme Precipitation Simulation)
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13 pages, 8725 KiB  
Article
Impacts of Cumulus Parameterizations on Extreme Precipitation Simulation in Semi-Arid Region: A Case Study in Northwest China
by Pinghan Zhaoye, Kai Yang and Chenghai Wang
Atmosphere 2022, 13(9), 1464; https://doi.org/10.3390/atmos13091464 - 9 Sep 2022
Cited by 4 | Viewed by 1927
Abstract
In the context of climate change, extreme precipitation in semi-arid region happens frequently. How well models simulate extreme precipitation in semi-arid region remains unclear. Based on a WRF v4.3 simulation of a rainstorm event that occurred in Qingyang, China on 21 July 2019, [...] Read more.
In the context of climate change, extreme precipitation in semi-arid region happens frequently. How well models simulate extreme precipitation in semi-arid region remains unclear. Based on a WRF v4.3 simulation of a rainstorm event that occurred in Qingyang, China on 21 July 2019, applying Kain–Fritsch (KF), Grell–Devenyi (GD) and Bullock–Wang (BW) schemes, the impacts of different cumulus parameterizations on extreme precipitation simulations in semi-arid region were analyzed, and the possible causes of precipitation biases were explored. The results showed that the WRF with the three schemes essentially reproduced the location and structure of precipitation, but the intensity of precipitation in the central region was underestimated. Based on the structure-amplitude-location (SAL) method, the KF scheme exhibited better performance in precipitation simulation than the other two schemes, while there were significant intensity and location deviations of rain band occurrence between simulations using the GD, BW schemes and observations. Convection simulation using the GD and BW schemes was less effective than that using the KF scheme, compared to the observations. As a result, the GD and BW schemes simulated a larger geopotential height at 500 hPa over Qingyang and weaker upper-level low troughs than simulations using the KF scheme. This led to simulation of less water vapor transport into the front of the trough, resulting in a deficit in simulated precipitation. The study results highlight the impacts of convection on large-scale atmospheric circulation linked to extreme precipitation in semi-arid region. Full article
(This article belongs to the Special Issue Improving Extreme Precipitation Simulation)
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