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Atmosphere
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Observations and Modeling of Precipitation Extremes and Tropical Cyclones
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Observations and Modeling of Precipitation Extremes and Tropical Cyclones

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

Deadline for manuscript submissions: closed (1 May 2024) | Viewed by 11074

Special Issue Editor

Dr. Muhammad Jehanzaib

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, Hanyang University, Seoul 04763, Republic of Korea
Interests: climate change; machine learning; drought propagation; rainfall-runoff modeling; climate extremes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Extreme precipitation events have increased in frequency and intensity across many regions of the world due to climate variations. The simulations of climate models also evidenced that precipitation extremes will intensify in the future in response to a warming climate. Various natural disasters such as Tropical Cyclones, Flooding, Droughts, Soil Erosion, and Landslides are associated with extreme precipitation events. Anthropogenic forcing has been shown to have contributed to the intensification of precipitation extremes over northern hemisphere land. Therefore, research on extreme precipitation has become a hot topic. Different approaches have been used to model extreme precipitations, such as Index analysis, Frequency analysis, and Spatial trend analysis. These methods use statistical technology to disperse the climatic factors into the related indices to examine the time interval of the recurrence of an extreme event for many years; thus, these methods are very significant to engineering design and planning. Further, the challenge of modeling dynamics needs to be addressed in extreme precipitation analysis. The core aim of this Special Issue is to contribute novel modeling frameworks as well as innovative approaches for extreme precipitation modeling in the field of meteorology and safeguarding water resources under climate change.

Dr. Muhammad Jehanzaib
Guest Editor

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Keywords

  • climate extremes
  • droughts
  • floods
  • non-stationarity
  • climate change
  • Anthropocene
  • typhoon
  • extreme events
  • forecasting
  • machine learning
  • frequency analysis
  • statistical modeling

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Published Papers (7 papers)

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Research

15 pages, 3719 KiB  
Article
Impact of High-Resolution Land Cover on Simulation of a Warm-Sector Torrential Rainfall Event in Guangzhou
by Ning Wang, Yanan Liu, Fan Ping and Jiahua Mao
Atmosphere 2024, 15(6), 687; https://doi.org/10.3390/atmos15060687 - 4 Jun 2024
Viewed by 646
Abstract
This study on the warm-sector heavy rainfall event in Guangzhou on 7 May 2017, examined the effects and mechanisms of incorporating 30 m high-resolution land surface data into its numerical simulation. The updated 1km numerical model, integrating 30 m high-resolution land surface data, [...] Read more.
This study on the warm-sector heavy rainfall event in Guangzhou on 7 May 2017, examined the effects and mechanisms of incorporating 30 m high-resolution land surface data into its numerical simulation. The updated 1km numerical model, integrating 30 m high-resolution land surface data, successfully captured the initiation, back-building, and organized development of warm-sector convections in Huadu and Zengcheng District. The analysis revealed that the high spatial resolution of the surface data led to a reduced urban area footprint (urban −6.31%), increased vegetation cover (forest 11.63%, croplands 1%), and enhanced surface runoff (water 2.77%) compared with a model’s default land cover (900 m). These changes mitigated the urban heat island (UHI) effect within the metropolitan area and decreased the surface sensible heat flux. This reduction contributed to a pronounced temperature gradient between Huadu Mountain and the urban area. Additionally, a stronger high-pressure recirculation and sea–land breezes facilitated the transport of warm and moist air from the sea inland, creating a humid corridor along the sea–land interface. The consistent influx of warm and moist air near the mountain front, where strong temperature gradients were present, forcibly triggered warm-sector convection, intensifying its organization. This study highlighted the critical role of high-resolution land surface data in the accurate numerical simulation of warm-sector heavy rainfall. Full article
(This article belongs to the Special Issue Observations and Modeling of Precipitation Extremes and Tropical Cyclones)
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15 pages, 10282 KiB  
Article
Investigation on the Sensitivity of Precipitation Simulation to Model Parameterization and Analysis Nudging over Hebei Province, China
by Yuanhua Li, Zhiguang Tian, Xia Chen, Xiashu Su and Entao Yu
Atmosphere 2024, 15(4), 512; https://doi.org/10.3390/atmos15040512 - 22 Apr 2024
Viewed by 1018
Abstract
The physical parameterizations have important influence on model performance in precipitation simulation and prediction; however, previous investigations are seldom conducted at very high resolution over Hebei Province, which is often influenced by extreme events such as droughts and floods. In this paper, the [...] Read more.
The physical parameterizations have important influence on model performance in precipitation simulation and prediction; however, previous investigations are seldom conducted at very high resolution over Hebei Province, which is often influenced by extreme events such as droughts and floods. In this paper, the influence of parameterization schemes and analysis nudging on precipitation simulation is investigated using the WRF (weather research and forecasting) model with many sensitivity experiments at the cumulus “gray-zone” resolution (5 km). The model performance of different sensitivity simulations is determined by a comparison with the local high-quality observational data. The results indicate that the WRF model generally reproduces the distribution of precipitation well, and the model tends to underestimate precipitation compared with the station observations. The sensitivity simulation with the Tiedtke cumulus parameterization scheme combined with the Thompson microphysics scheme shows the best model performance, with the highest temporal correlation coefficient (0.45) and lowest root mean square error (0.34 mm/day). At the same time, analysis nudging, which incorporates observational information into simulation, can improve the model performance in precipitation simulation. Further analysis indicates that the negative bias in precipitation may be associated with the negative bias in relative humidity, which in turn is associated with the positive bias in temperature and wind speed. This study highlights the role of parameterization schemes and analysis nudging in precipitation simulation and provides a valuable reference for further investigations on precipitation forecasting applications. Full article
(This article belongs to the Special Issue Observations and Modeling of Precipitation Extremes and Tropical Cyclones)
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18 pages, 9663 KiB  
Article
Precipitation and Moisture Transport of the 2021 Shimokita Heavy Precipitation: A Transformed Extratropical Cyclone from Typhoon#9
by Akiyo Yatagai and Shogo Saruta
Atmosphere 2024, 15(1), 94; https://doi.org/10.3390/atmos15010094 - 11 Jan 2024
Cited by 1 | Viewed by 1206
Abstract
This study examines the heavy rainfall event that occurred in the Shimokita Peninsula, Japan, on 9–10 August 2021, resulting from an extra-tropical cyclone that developed from Typhoon#9 (EC9). The objective of this study is to elucidate the relationship between moisture transport and heavy [...] Read more.
This study examines the heavy rainfall event that occurred in the Shimokita Peninsula, Japan, on 9–10 August 2021, resulting from an extra-tropical cyclone that developed from Typhoon#9 (EC9). The objective of this study is to elucidate the relationship between moisture transport and heavy rainfall and to verify the role of EC9. The authors created intensive hourly precipitation data over the Aomori Prefecture and analyzed them together with moisture fields. In most locations where the landslide disaster occurred, there were two precipitation peaks: at 9 UTC and 18 UTC on 9 August. The wind shear was strong from the lower to the upper troposphere with easterly winds in the lower troposphere and warm moist air from south for the first peak. A strong horizontal gradient of equivalent potential temperature, a northerly in lower troposphere, and moisture convergence over Shimokita Peninsula indicate the existence of the stationary front for the latter peak (18 UTC). The heavy precipitation and moisture convergence that caused the Shimokita event were identified by the stationary front of EC9 around the latter peak (15 UTC of 9th–06 UTC of 10 August). The precipitation distribution, which has a precipitation peak northeast of the EC center, is a typical typhoon-turned extratropical cyclone (EC) precipitation distribution. Full article
(This article belongs to the Special Issue Observations and Modeling of Precipitation Extremes and Tropical Cyclones)
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15 pages, 13233 KiB  
Communication
Challenges in the Forecasting of Severe Typhoon Koinu in 2023
by Yu-Heng He and Pak-Wai Chan
Atmosphere 2024, 15(1), 31; https://doi.org/10.3390/atmos15010031 - 27 Dec 2023
Cited by 2 | Viewed by 1770
Abstract
Hong Kong was under the direct hit of Severe Typhoon Koinu (2314) on 8 and 9 October 2023, necessitating the issuance of the Increasing Gale or Storm Signal, No. 9. Koinu was a very challenging case for TC forecasting and warning services due [...] Read more.
Hong Kong was under the direct hit of Severe Typhoon Koinu (2314) on 8 and 9 October 2023, necessitating the issuance of the Increasing Gale or Storm Signal, No. 9. Koinu was a very challenging case for TC forecasting and warning services due to its compact size and erratic movement over the northern part of the South China Sea. This paper reviews the difficulties and challenges of the forecasting aspect of the severe typhoon. The predicted tropical cyclone track and intensity from both conventional models and emerging artificial intelligence models are examined, as well as local wind and rainfall forecast. Experience in this case study showed that while deterministic global models only performed moderately and were not able to adequately support early warning, a regional model and AI models could more effectively support decision making for an operational tropical cyclone warning service. Full article
(This article belongs to the Special Issue Observations and Modeling of Precipitation Extremes and Tropical Cyclones)
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27 pages, 18249 KiB  
Article
Hindcast Insights from Storm Surge Forecasting of Super Typhoon Saola (2309) in Hong Kong with the Sea, Lake and Overland Surges from Hurricanes Model
by Dick-Shum Lau, Wai-Soen Chan, Yat-Chun Wong, Ching-Chi Lam and Pak-Wai Chan
Atmosphere 2024, 15(1), 17; https://doi.org/10.3390/atmos15010017 - 22 Dec 2023
Cited by 4 | Viewed by 1666
Abstract
Super Typhoon Saola (2309) skirted past south-southeast of Hong Kong within 40 km on the night of 1 September 2023, posing a significant storm surge threat to Hong Kong. Given the close proximity of Saola with a peak intensity of about 210 km/h [...] Read more.
Super Typhoon Saola (2309) skirted past south-southeast of Hong Kong within 40 km on the night of 1 September 2023, posing a significant storm surge threat to Hong Kong. Given the close proximity of Saola with a peak intensity of about 210 km/h within 300 km of Hong Kong, a close call of the “super typhoon direct-hit” scenario, this case provides valuable insights from a hindcast review of storm surge forecasts and warning operation using the Sea, Lake and Overland Surges from Hurricanes (SLOSH) model, which is the operational storm surge model adopted by the Hong Kong Observatory (HKO). The performance of the HKO’s PRobabilistic Inundation Map Evaluation System (PRIMES) using both statistical and model ensemble approaches was also reviewed in this paper. Saola was a challenging case for operational forecasting of a compact TC structure with changes in storm size and intensity when it came close to Hong Kong. With major observations of storm structure using weather radar and dense automatic weather station, tide gauge and water level gauge networks, the high sensitivity of storm surge forecasts to the storm size parameter and the distance of closest approach was clearly revealed in the case of Saola. Even with a circularly symmetric TC parametric model like SLOSH, the hindcast review results illustrated that the model outputs were reasonably accurate during the closest approach of Saola given an accurate storm size and distance of closest approach were input, and using a highly computationally efficient storm surge model made it possible for the nowcasting of storm surges to handle compact and intense TC direct-hit cases in operational TC forecasting. Taking a nowcasting approach not only helps provide more reliable storm tide forecasts, but also facilitates the formulation of a better warning strategy when making final-call decisions in emergency response actions, based on the more frequent real-time analysis of TC position, intensity and storm size and the more accurate prediction of these parameters. A nowcasting workflow for storm surge operation was proposed in this paper. Full article
(This article belongs to the Special Issue Observations and Modeling of Precipitation Extremes and Tropical Cyclones)
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23 pages, 6963 KiB  
Article
Hydrological Drought Prediction Based on Hybrid Extreme Learning Machine: Wadi Mina Basin Case Study, Algeria
by Mohammed Achite, Okan Mert Katipoğlu, Muhammad Jehanzaib, Nehal Elshaboury, Veysi Kartal and Shoaib Ali
Atmosphere 2023, 14(9), 1447; https://doi.org/10.3390/atmos14091447 - 17 Sep 2023
Cited by 10 | Viewed by 1845
Abstract
Drought is one of the most severe climatic calamities, affecting many aspects of the environment and human existence. Effective planning and decision making in disaster-prone areas require accurate and reliable drought predictions globally. The selection of an effective forecasting model is still challenging [...] Read more.
Drought is one of the most severe climatic calamities, affecting many aspects of the environment and human existence. Effective planning and decision making in disaster-prone areas require accurate and reliable drought predictions globally. The selection of an effective forecasting model is still challenging due to the lack of information on model performance, even though data-driven models have been widely employed to anticipate droughts. Therefore, this study investigated the application of simple extreme learning machine (ELM) and wavelet-based ELM (W-ELM) algorithms in drought forecasting. Standardized runoff index was used to model hydrological drought at different timescales (1-, 3-, 6-, 9-, and 12-month) at five Wadi Mina Basin (Algeria) hydrological stations. A partial autocorrelation function was adopted to select lagged input combinations for drought prediction. The results suggested that both algorithms predict hydrological drought well. Still, the performance of W-ELM remained superior at most of the hydrological stations with an average coefficient of determination = 0.74, root mean square error = 0.36, and mean absolute error = 0.43. It was also observed that the performance of the models in predicting drought at the 12-month timescale was higher than at the 1-month timescale. The proposed hybrid approach combined ELM’s fast-learning ability and discrete wavelet transform’s ability to decompose into different frequency bands, producing promising outputs in hydrological droughts. The findings indicated that the W-ELM model can be used for reliable drought predictions in Algeria. Full article
(This article belongs to the Special Issue Observations and Modeling of Precipitation Extremes and Tropical Cyclones)
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14 pages, 2145 KiB  
Article
Research on Typhoon Precipitation Prediction over Hainan Island Based on Dynamical–Statistical–Analog Technology
by Xianling Jiang, Yunqi Ma, Fumin Ren, Chenchen Ding, Jing Han and Juan Shi
Atmosphere 2023, 14(8), 1210; https://doi.org/10.3390/atmos14081210 - 27 Jul 2023
Viewed by 1233
Abstract
Based on the Dynamical–Statistical–Analog Ensemble Forecast model for Landfalling Typhoon Precipitation (DSAEF_LTP model), the optimal forecast scheme for the tropical cyclone (TC) accumulated precipitation over Hainan Island, China (DSAEF_LTP_HN) is established. To test the forecasting performance of DSAEF_LTP_HN, its forecasting results are compared [...] Read more.
Based on the Dynamical–Statistical–Analog Ensemble Forecast model for Landfalling Typhoon Precipitation (DSAEF_LTP model), the optimal forecast scheme for the tropical cyclone (TC) accumulated precipitation over Hainan Island, China (DSAEF_LTP_HN) is established. To test the forecasting performance of DSAEF_LTP_HN, its forecasting results are compared with other numerical models. The average threat score (TS) of accumulated precipitation forecast by DSAEF_LTP_HN is compared with other numerical models over independent samples. The results show that for accumulated precipitation ≥ 100 mm, the TS produced by DSAEF_LTP_HN reaches 0.39, ranking first, followed by ECMWF (0.36). For accumulated precipitation ≥ 250 mm, the TS of DSAEF_LTP_HN (0.04) is second only to ECMWF (0.19). Further analysis reveals that the forecasting performance of DSAEF_LTP_HN for TC precipitation is closely related to the TC characteristics. The longer the TC impacts Hainan Island and the heavier the precipitation delivered to Hainan Island, the better the forecasting performance of DSAEF_LTP_HN is. DSAEF_LTP_HN can successfully capture the center of heavy precipitation. However, there is still a phenomenon of false forecasts for some TC heavy precipitation, which requires further improvement of the model in the future. Full article
(This article belongs to the Special Issue Observations and Modeling of Precipitation Extremes and Tropical Cyclones)
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