Numerical Modeling and Statistical Analysis of Severe Weather Conditions and Extreme Events

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 (31 August 2021) | Viewed by 18670

Special Issue Editors


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Guest Editor
National Research Council of Italy, Institute of Atmospheric Sciences and Climate (CNR-ISAC), Zona Industriale ex SIR, 88046 Lamezia Terme, Italy
Interests: mesoscale meteorological modeling; severe weather; numerical weather prediction
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Guest Editor
National Research Council of Italy, Institute of Atmospheric Sciences and Climate (CNR-ISAC), Via del Fosso del Cavaliere 100, Rome, Italy
Interests: numerical weather prediction; data assimilation; lightning forecast; precipitation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to collect state-of-the-art contributions on interdisciplinary applications of mesoscale numerical weather prediction (NWP) models for the study of atmospheric physical processes related to severe weather conditions and extreme events.

A wide range of weather-related topics and techniques are welcome. Possible topics include but are not limited to: heavy precipitation systems and triggering mechanisms, floods, landslides, supercell thunderstorms, windstorms, tornados and downbursts, convection initiation, mesoscale convective systems, statistical analysis, model verification and performance, impact of data assimilation techniques on the model performance at different forecast ranges, model sensitivity tests, etc.

Studies based on coupled modeling systems are considered very useful, due to the important role of the mesoscale NWP models in driving (or being coupled to) other Earth-science-related numerical tools, such as modeling systems devoted to oceanography, hydrology, air quality, regional climate, etc.

A perspective related to climate change is also encouraged, in order to discuss a possible increase, in both intensity and frequency, of severe weather events.

Dr. Elenio Avolio
Dr. Stefano Federico
Guest Editors

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Keywords

  • Severe weather conditions
  • Extreme meteorological events
  • Flash flood
  • Numerical weather prediction (NWP)
  • Deep convection
  • Data assimilation
  • Model verification
  • Model sensitivity tests

Published Papers (6 papers)

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Research

16 pages, 1996 KiB  
Article
South America Seasonal Precipitation Prediction by Gradient-Boosting Machine-Learning Approach
by Vinicius Schmidt Monego, Juliana Aparecida Anochi and Haroldo Fraga de Campos Velho
Atmosphere 2022, 13(2), 243; https://doi.org/10.3390/atmos13020243 - 31 Jan 2022
Cited by 10 | Viewed by 2944
Abstract
Machine learning has experienced great success in many applications. Precipitation is a hard meteorological variable to predict, but it has a strong impact on society. Here, a machine-learning technique—a formulation of gradient-boosted trees—is applied to climate seasonal precipitation prediction over South America. The [...] Read more.
Machine learning has experienced great success in many applications. Precipitation is a hard meteorological variable to predict, but it has a strong impact on society. Here, a machine-learning technique—a formulation of gradient-boosted trees—is applied to climate seasonal precipitation prediction over South America. The Optuna framework, based on Bayesian optimization, was employed to determine the optimal hyperparameters for the gradient-boosting scheme. A comparison between seasonal precipitation forecasting among the numerical atmospheric models used by the National Institute for Space Research (INPE, Brazil) as an operational procedure for weather/climate forecasting, gradient boosting, and deep-learning techniques is made regarding observation, with some showing better performance for the boosting scheme. Full article
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16 pages, 6635 KiB  
Article
A Numerical Simulation of the “1907” Kaiyuan Tornado Weather Process in Liaoning, Northeast China
by Yiping Wang, Tong Wang, Pu Yang and Wei Xue
Atmosphere 2022, 13(2), 219; https://doi.org/10.3390/atmos13020219 - 28 Jan 2022
Cited by 12 | Viewed by 2374
Abstract
From 17:00 to 18:00 local standard time (LST) on 3 July 2019, a rare strong tornado occurred in Kaiyuan, Liaoning Province, northeast China. NCEP/NCAR 0.25° × 0.25° reanalysis data and WRF4.0 numerical prediction models were used to carry out the numerical simulation. Double [...] Read more.
From 17:00 to 18:00 local standard time (LST) on 3 July 2019, a rare strong tornado occurred in Kaiyuan, Liaoning Province, northeast China. NCEP/NCAR 0.25° × 0.25° reanalysis data and WRF4.0 numerical prediction models were used to carry out the numerical simulation. Double nesting was adopted, and the horizontal grid distance was 9 km by 3 km. Based on the observation data of China meteorological observation stations, surface and upper charts, Doppler radar data, Himawari(HMW)-8 satellite images and numerical simulation results, the mesoscale structure and mechanism of the tornado were studied. The results show that: (1) At the northwest edge of the subtropical high, and the northeast cold vortex located in Northeast China, when the transverse trough moves southward, cold air is supplied continuously. Under the joint influence of the surface northeast cyclone, these are the main synoptic features of the tornado; (2) The northeast cold vortex cloud system was located at the junction of Heilongjiang and Jilin Provinces, and a squall line cloud system is formed. The tornado occurred at the tail of the squall line, and the strongest echo reached 65 dBZ. A mesocyclone, a 20 km northwest–southeast convergence belt, V-shaped gap, echo overhang structure and tornado vortex feature (TVS) were detected by the Doppler radar; (3) Before the tornado occurred, dry and cold air intruded from the northwest of the cold vortex, and a water vapor convergence zone appeared south of the squall line. The water vapor saturation zone with 80% relative humidity in northeast China was concentrated at 700 hPa, and the 20% dry column dropped down to 500 hPa between 115 and 124° E from the west. On the 850 hPa physical fields, there was a −20 × 10−5 s−1 convergence zone, and a 16 × 10−5 s−1 divergence belt appeared south and north of the squall line. A negative vorticity belt and a positive vorticity belt appeared south and north of the squall line, respectively. Kaiyuan is located at the smallest vertical shear, which is the junction place of three large vertical shear belts; (4) After 10:00 LST, the westerly wind 20 (10) m·s−1 dropped to 400 (800) hPa between 126 and 127° E. The northerly gale at 300 hPa north of 45° N moved southward. The rising center of the low level at 17:00 LST at approximately 45° N moved southward, and a sinking center appeared above it; (5) Several pairs of positive and negative vorticity columns formed between the lower troposphere and the place where the tornado occurred. There was convective instability at the lower level. CAPE increased, 0–3 km vertical wind shear increased, and LCL decreased remarkably during the afternoon. Full article
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23 pages, 4843 KiB  
Article
High-Rate Precipitation Occurrence Modulated by Solar Wind High-Speed Streams
by Paul Prikryl, Vojto Rušin and Emil A. Prikryl
Atmosphere 2021, 12(9), 1186; https://doi.org/10.3390/atmos12091186 - 15 Sep 2021
Cited by 1 | Viewed by 2814
Abstract
Extreme weather events, such as heavy rainfall causing floods and flash floods continue to present difficult challenges in forecasting. Using gridded daily precipitation datasets in conjunction with solar wind data it is shown that high-rate precipitation occurrence is modulated by solar wind high-speed [...] Read more.
Extreme weather events, such as heavy rainfall causing floods and flash floods continue to present difficult challenges in forecasting. Using gridded daily precipitation datasets in conjunction with solar wind data it is shown that high-rate precipitation occurrence is modulated by solar wind high-speed streams. Superposed epoch analysis shows a statistical increase in the occurrence of high-rate precipitation following arrivals of high-speed streams from coronal holes, including their recurrence with the solar rotation period of 27 days. These results are consistent with the observed tendency of heavy rainfall leading to floods and flash floods in Japan, Australia, and continental United States to follow arrivals of high-speed streams. A possible role of the solar wind–magnetosphere–ionosphere–atmosphere coupling in weather as mediated by globally propagating aurorally excited atmospheric gravity waves triggering conditional moist instabilities leading to convection in the troposphere that has been proposed in previous publications is highlighted. Full article
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21 pages, 6689 KiB  
Article
Impact of Radar Reflectivity and Lightning Data Assimilation on the Rainfall Forecast and Predictability of a Summer Convective Thunderstorm in Southern Italy
by Stefano Federico, Rosa Claudia Torcasio, Silvia Puca, Gianfranco Vulpiani, Albert Comellas Prat, Stefano Dietrich and Elenio Avolio
Atmosphere 2021, 12(8), 958; https://doi.org/10.3390/atmos12080958 - 26 Jul 2021
Cited by 8 | Viewed by 2238
Abstract
Heavy and localized summer events are very hard to predict and, at the same time, potentially dangerous for people and properties. This paper focuses on an event occurred on 15 July 2020 in Palermo, the largest city of Sicily, causing about 120 mm [...] Read more.
Heavy and localized summer events are very hard to predict and, at the same time, potentially dangerous for people and properties. This paper focuses on an event occurred on 15 July 2020 in Palermo, the largest city of Sicily, causing about 120 mm of rainfall in 3 h. The aim is to investigate the event predictability and a potential way to improve the precipitation forecast. To reach this aim, lightning (LDA) and radar reflectivity data assimilation (RDA) was applied. LDA was able to trigger deep convection over Palermo, with high precision, whereas the RDA had a key role in the prediction of the amount of rainfall. The simultaneous assimilation of both data sources gave the best results. An alert for a moderate–intense forecast could have been issued one hour and a half before the storm developed over the city, even if predicting only half of the total rainfall. A satisfactory prediction of the amount of rainfall could have been issued at 14:30 UTC, when precipitation was already affecting the city. Although the study is centered on a single event, it highlights the need for rapidly updated forecast cycles with data assimilation at the local scale, for a better prediction of similar events. Full article
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22 pages, 1090 KiB  
Article
Sensitivity of Tropical Cyclone Idai Simulations to Cumulus Parametrization Schemes
by Mary-Jane M. Bopape, Hipolito Cardoso, Robert S. Plant, Elelwani Phaduli, Hector Chikoore, Thando Ndarana, Lino Khalau and Edward Rakate
Atmosphere 2021, 12(8), 932; https://doi.org/10.3390/atmos12080932 - 21 Jul 2021
Cited by 8 | Viewed by 3750
Abstract
Weather simulations are sensitive to subgrid processes that are parameterized in numerical weather prediction (NWP) models. In this study, we investigated the response of tropical cyclone Idai simulations to different cumulus parameterization schemes using the Weather Research and Forecasting (WRF) model with a [...] Read more.
Weather simulations are sensitive to subgrid processes that are parameterized in numerical weather prediction (NWP) models. In this study, we investigated the response of tropical cyclone Idai simulations to different cumulus parameterization schemes using the Weather Research and Forecasting (WRF) model with a 6 km grid length. Seventy-two-hour (00 UTC 13 March to 00 UTC 16 March) simulations were conducted with the New Tiedtke (Tiedtke), New Simplified Arakawa–Schubert (NewSAS), Multi-Scale Kain–Fritsch (MSKF), Grell–Freitas, and the Betts–Miller–Janjic (BMJ) schemes. A simulation for the same event was also conducted with the convection scheme switched off. The twenty-four-hour accumulated rainfall during all three simulated days was generally similar across all six experiments. Larger differences in simulations were found for rainfall events away from the tropical cyclone. When the resolved and convective rainfall are partitioned, it is found that the scale-aware schemes (i.e., Grell–Freitas and MSKF) allow the model to resolve most of the rainfall, while they are less active. Regarding the maximum wind speed, and minimum sea level pressure (MSLP), the scale aware schemes simulate a higher intensity that is similar to the Joint Typhoon Warning Center (JTWC) dataset, however, the timing is more aligned with the Global Forecast System (GFS), which is the model providing initial conditions and time-dependent lateral boundary conditions. Simulations with the convection scheme off were found to be similar to those with the scale-aware schemes. It was found that Tiedtke simulates the location to be farther southwest compared to other schemes, while BMJ simulates the path to be more to the north after landfall. All of the schemes as well as GFS failed to simulate the movement of Idai into Zimbabwe, showing the potential impact of shortcomings on the forcing model. Our study shows that the use of scale aware schemes allows the model to resolve most of the dynamics, resulting in higher weather system intensity in the grey zone. The wrong timing of the peak shows a need to use better performing global models to provide lateral boundary conditions for downscalers. Full article
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12 pages, 3531 KiB  
Article
Characteristics of Extratropical Cyclones That Cause Tornadoes in Italy: A Preliminary Study
by Eigo Tochimoto, Mario Marcello Miglietta, Leonardo Bagaglini, Roberto Ingrosso and Hiroshi Niino
Atmosphere 2021, 12(2), 180; https://doi.org/10.3390/atmos12020180 - 29 Jan 2021
Cited by 4 | Viewed by 2652
Abstract
Characteristics of extratropical cyclones that cause tornadoes in Italy are investigated. Tornadoes between 2007 and 2016 are analyzed, and statistical analysis of the associated cyclone structures and environments is performed using the JRA-55 reanalysis. Tornadoes are distributed sporadically around the cyclone location within [...] Read more.
Characteristics of extratropical cyclones that cause tornadoes in Italy are investigated. Tornadoes between 2007 and 2016 are analyzed, and statistical analysis of the associated cyclone structures and environments is performed using the JRA-55 reanalysis. Tornadoes are distributed sporadically around the cyclone location within a window of 10° × 10°. The difference in the cyclone tracks partially explains the seasonal variability in the distribution of tornadoes. The highest number of tornadoes occur south of the cyclone centers, mainly in the warm sector, while a few are observed along the cold front. Composite mesoscale parameters are examined to identify the environmental conditions associated with tornadoes in different seasons. Potential instability is favorable to tornado development in autumn. The highest convective available potential energy (CAPE) in this season is associated with relatively high-temperature and humidity at low-levels, mainly due to the strong evaporation over the warm Mediterranean Sea. Upper-level potential vorticity (PV) anomalies and the associated cold air reduce the static stability above the cyclone center, mainly in spring and winter. On average, the values of CAPE are lower than for US tornadoes and comparable with those occurring in Japan, while storm relative helicity (SREH) is comparable with US tornadoes and higher than Japanese tornadoes, indicating that the environmental conditions for Italian tornadoes have peculiar characteristics. Overall, the conditions emerging in this study are close to the high-shear, low-CAPE environments typical of cool-season tornadoes in the Southeastern US. Full article
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