Topic Editors

Meteorology Laboratory, CIRA Italian Aerospace Research Center, 81043 Capua, CE, Italy
Meteorology Laboratory, CIRA Italian Aerospace Research Center, 81043 Capua, CE, Italy

Numerical Models and Weather Extreme Events

Abstract submission deadline
closed (25 November 2023)
Manuscript submission deadline
closed (25 January 2024)
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12474

Topic Information

Dear Colleagues,

This Topic comprises several interdisciplinary research areas that cover the main aspects of numerical weather predictions. Every year, there are hurricanes, extreme heat waves, tornadoes, and other extreme weather events, resulting in thousands of deaths and billions of dollars in damage. The prediction of extreme weather further in advance and with increased accuracy could allow targeted regions to be better prepared in order to reduce loss of life and property damage. It is evident that climate change is increasing the intensity and frequency of extreme weather events; thus, their prompt prediction has never been more important. The development of accurate local forecasts is notoriously difficult due to the complex physics driving heavy precipitation and intense winds. Weather forecasting requires supercomputers and trained local practitioners, thus narrowing its accessibility to wealthy governments and communities. Moreover, traditional weather forecasts with a predictive scope of several days in advance are very coarse in terms of resolution and, therefore, do not capture local extreme events. One alternative developed in recent years is the usage of local observations to forecast weather up to a couple of hours in advance. In this regard, next-generation satellites bring great opportunities to further improve short-term forecasting. Artificial intelligence and machine-learning breakthroughs are changing weather forecasting such that resource-heavy, regional weather models might soon be completely replaced by machine-learning approaches. Such innovative approaches use specific networks (GANs) trained via global weather forecasts to correct for the biases that exist in current weather models. The new model downscales global forecasts to be as accurate as a local forecast, without requiring the vast amounts of computational, financial, and human resources previously required for such a small scale. Manuscripts addressing these exciting areas can be submitted.

Some examples of related subjects include:

  • Current challenging areas in weather models;
  • The assessment of a weather model’s ability to represent extreme weather events;
  • Supercomputing applied to Weather Forecasting;
  • Ensemble modeling;
  • Monte Carlo simulations;
  • Stochastic weather generators;
  • The monitoring of weather and climate from space.

Dr. Edoardo Bucchignani
Dr. Andrea Mastellone
Topic Editors

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Applied Sciences
applsci
2.7 4.5 2011 16.9 Days CHF 2400
Atmosphere
atmosphere
2.9 4.1 2010 17.7 Days CHF 2400
Climate
climate
3.7 5.2 2013 19.7 Days CHF 1800
Meteorology
meteorology
- - 2022 28.3 Days CHF 1000
Remote Sensing
remotesensing
5.0 7.9 2009 23 Days CHF 2700

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Published Papers (10 papers)

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17 pages, 8923 KiB  
Article
Idealized Simulations of a Supercell Interacting with an Urban Area
by Jason Naylor, Megan E. Berry and Emily G. Gosney
Meteorology 2024, 3(1), 97-113; https://doi.org/10.3390/meteorology3010005 - 07 Mar 2024
Viewed by 456
Abstract
Idealized simulations with a cloud-resolving model are conducted to examine the impact of a simplified city on the structure of a supercell thunderstorm. The simplified city is created by enhancing the surface roughness length and/or surface temperature relative to the surroundings. When the [...] Read more.
Idealized simulations with a cloud-resolving model are conducted to examine the impact of a simplified city on the structure of a supercell thunderstorm. The simplified city is created by enhancing the surface roughness length and/or surface temperature relative to the surroundings. When the simplified city is both warmer and has larger surface roughness relative to its surroundings, the supercell that passes over it has a larger updraft helicity (at both midlevels and the surface) and enhanced precipitation and hail downwind of the city, all relative to the control simulation. The storm environment within the city has larger convective available potential energy which helps stimulate stronger low-level updrafts. Storm relative helicity (SRH) is actually reduced over the city, but enhanced in a narrow band on the northern edge of the city. This band of larger SRH is ingested by the primary updraft just prior to passing over the city, corresponding with enhancement to the near-surface mesocyclone. Additional simulations in which the simplified city is altered by removing either the heat island or surface roughness length gradient reveal that the presence of a heat island is most closely associated with enhancements in updraft helicity and low-level updrafts relative to the control simulation. Full article
(This article belongs to the Topic Numerical Models and Weather Extreme Events)
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23 pages, 2305 KiB  
Article
Downscaling Climatic Variables at a River Basin Scale: Statistical Validation and Ensemble Projection under Climate Change Scenarios
by Renalda El-Samra, Abeer Haddad, Ibrahim Alameddine, Elie Bou-Zeid and Mutasem El-Fadel
Climate 2024, 12(2), 27; https://doi.org/10.3390/cli12020027 - 14 Feb 2024
Viewed by 1499
Abstract
Climatic statistical downscaling in arid and topographically complex river basins remains relatively lacking. To address this gap, climatic variables derived from a global climate model (GCM) ensemble were downscaled from a grid resolution of 2.5° × 2.5° down to the station level. For [...] Read more.
Climatic statistical downscaling in arid and topographically complex river basins remains relatively lacking. To address this gap, climatic variables derived from a global climate model (GCM) ensemble were downscaled from a grid resolution of 2.5° × 2.5° down to the station level. For this purpose, a combination of multiple linear and logistic regressions was developed, calibrated and validated with regard to their predictions of monthly precipitation and daily temperature in the Jordan River Basin. Seasonal standardized predictors were selected using a backward stepwise regression. The validated models were used to examine future scenarios based on GCM simulations under two Representative Concentration Pathways (RCP4.5 and RCP8.5) for the period 2006–2050. The results showed a cumulative near-surface air temperature increase of 1.54 °C and 2.11 °C and a cumulative precipitation decrease of 100 mm and 135 mm under the RCP4.5 and RCP8.5, respectively, by 2050. This pattern will inevitably add stress to water resources, increasing management challenges in the semi-arid to arid regions of the basin. Moreover, the current application highlights the potential of adopting regression-based models to downscale GCM predictions and inform future water resources management in poorly monitored arid regions at the river basin scale. Full article
(This article belongs to the Topic Numerical Models and Weather Extreme Events)
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26 pages, 13413 KiB  
Article
Evaluation of Bias-Corrected GCM CMIP6 Simulation of Sea Surface Temperature over the Gulf of Guinea
by Oye Ideki and Anthony R. Lupo
Climate 2024, 12(2), 19; https://doi.org/10.3390/cli12020019 - 31 Jan 2024
Viewed by 1536
Abstract
This study used an ERA5 reanalysis SST dataset re-gridded to a common grid with a 0.25° × 0.25° spatial resolution (latitude × longitude) for the historical (1940–2014) and projected (2015–2100) periods. The SST simulation under the SSP5-8.5 scenario was carried out with outputs [...] Read more.
This study used an ERA5 reanalysis SST dataset re-gridded to a common grid with a 0.25° × 0.25° spatial resolution (latitude × longitude) for the historical (1940–2014) and projected (2015–2100) periods. The SST simulation under the SSP5-8.5 scenario was carried out with outputs from eight General Circulation Models (GCMs). The bias-corrected dataset was developed using Empirical Quantile Mapping (EQM) for the historical (1940–2015) and future (2030–2100) periods while the CMIP6 model simulation was evaluated against the ERA5 monthly observed reanalysis data for temperatures over the Gulf of Guinea. Overall, the CMIP6 models’ future simulations in 2030–20100 based on the SSP5-8.5 scenario indicate that SSTs are projected, for the Gulf of Guinea, to increase by 4.61 °C, from 31 °C in the coast in 2030 to 35 °C in 2100, and 2.6 °C in the Western GOG (Sahel). The Linux-based Ncview, Ferret, and the CDO (Climate Data Operator) software packages were used to perform further data re-gridding and assess statistical functions concerning the data. In addition, ArcGIS was used to develop output maps for visualizing the spatial trends of the historical and future outputs of the GCM. The correlation coefficient (r) was used to evaluate the performance of the CMIP6 models, and the analysis showed ACCESS 0.1, CAMS CSM 0.2, CAN ESM 0.3, CMCC 0.3, and MCM 0.4, indicating that all models performed well in capturing the climatological patterns of the SSTs. The CMIP6 bias-corrected model simulations showed that increased SST warming over the GOG will be higher in the far period than the near-term climate scenario. This study affirms that the CMIP6 projections can be used for multiple assessments related to climate and hydrological impact studies and for the development of mitigation measures under a warming climate. Full article
(This article belongs to the Topic Numerical Models and Weather Extreme Events)
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24 pages, 20856 KiB  
Article
Improved Gravity Wave Drag to Enhance Precipitation Simulation: A Case Study of Typhoon In-Fa
by Kun Liu, Fei Yu, Yong Su, Hongliang Zhang, Qiying Chen and Jian Sun
Atmosphere 2023, 14(12), 1801; https://doi.org/10.3390/atmos14121801 - 08 Dec 2023
Viewed by 671
Abstract
Traditional gravity wave drag parameterizations produce wind stresses that are insensitive to changing horizontal resolution in numerical weather prediction (NWP), partly due to the idealized elliptical assumption. This study employs the modified subgrid-scale orography scheme based on the Fourier transform into gravity wave [...] Read more.
Traditional gravity wave drag parameterizations produce wind stresses that are insensitive to changing horizontal resolution in numerical weather prediction (NWP), partly due to the idealized elliptical assumption. This study employs the modified subgrid-scale orography scheme based on the Fourier transform into gravity wave drag scheme of the China Meteorological Administration Global Forecast System (CMA-GFS) to assess its impacts on simulating precipitation during the slow-moving period of Typhoon In-Fa after its landfall in Zhejiang Province, China. The simulation with the updated scheme can effectively reduce the accumulated precipitation bias of the control one and improve the simulation of precipitation distribution and intensity, especially in the hourly precipitation simulation. The improved scheme primarily influences the wind field of the low-level troposphere and also changes the convergence of the integrated water vapor transport and ascending motions related to the reduced precipitation biases. The modified scheme enhances the tendencies of the horizontal winds caused by the varying horizontal resolutions in the model, strengthening the sensitivity of the gravity wave drag across the horizontal scales. Results from medium-range forecasts indicate the modified scheme benefits the statistics scores of precipitation over China and also reduces root-mean-square errors of 2 m temperature and 10 m winds. Full article
(This article belongs to the Topic Numerical Models and Weather Extreme Events)
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14 pages, 2636 KiB  
Article
Ability of the GRAPES Ensemble Forecast Product to Forecast Extreme Temperatures over the Tibetan Plateau
by Ruixin Wang, Yuxi Liang, Hongke Cai and Jiawen Zheng
Atmosphere 2023, 14(11), 1625; https://doi.org/10.3390/atmos14111625 - 29 Oct 2023
Cited by 1 | Viewed by 776
Abstract
Due to climate change, extreme temperature events are receiving increased attention. Based on the climate state deviation and threat score (TS), the ability of the Global/Regional Assimilation and Prediction System (GRAPES) ensemble model to forecast extreme temperature events was examined. The “optimal” Extreme [...] Read more.
Due to climate change, extreme temperature events are receiving increased attention. Based on the climate state deviation and threat score (TS), the ability of the Global/Regional Assimilation and Prediction System (GRAPES) ensemble model to forecast extreme temperature events was examined. The “optimal” Extreme Forecast Index (EFI) was derived for plateau forecasting, and its predictability was examined based on the receiver operating characteristic (ROC) curve method. Meanwhile, the applicability of the Shift of Tails (SOT) index to extreme temperature forecasting was analyzed using extreme temperature cases. Results showed that the GRAPES model has a warm bias for both summer extreme high temperature and winter extreme low temperature, and the warm bias decreases slightly with an increase in the forecasting lead time. The ensemble mean and median forecasts are less effective, and the maximum value is more predictable. However, for the ensemble forecast model, the extreme information in its forecast is more unstable, and the limitation of the extreme temperature forecast in the plateau region is higher. With different forecast lead times, the TS tends to increase and then decrease with an increase in the EFI threshold, which means that there is an optimal EFI. The optimal EFI thresholds for summer extreme high-temperature forecasts are all less than −0.5, while for winter extreme low-temperature forecasts, they are almost all less than 0. From the ROC curves, the EFI has a certain level of predictability for summer extreme high temperatures but poorer forecasting effects. Furthermore, the EFI has some predictability for extreme summer high temperatures, but the prediction effect is poor. For the extremely low temperatures in winter, which are poorly predicted by the model itself, post-processing of the extreme information predicted by the model with the EFI can improve the forecasting effect of the model. Through analysis of individual cases, it was found that the extreme intensity reflected by the SOT_+ (0.9) index of the model was closer to reality for the prediction of extremely high temperatures, whereas for the prediction of extremely low temperatures, the extreme intensity indicated by the SOT_− (0.1) index of the model was weaker. Therefore, the SOT index can play an important auxiliary role in the prediction of the intensity of extreme events based on the EFI. Full article
(This article belongs to the Topic Numerical Models and Weather Extreme Events)
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12 pages, 5630 KiB  
Article
Improving the Forecasts of Surface Latent Heat Fluxes and Surface Air Temperature in the GRAPES Global Forecast System
by Miaoling Liang, Xing Yuan and Wenyan Wang
Atmosphere 2023, 14(8), 1241; https://doi.org/10.3390/atmos14081241 - 02 Aug 2023
Cited by 1 | Viewed by 925
Abstract
The GRAPES (Global/Regional Assimilation and Prediction System) global medium-range forecast system (GRAPES_GFS) is a new generation numerical weather forecast model developed by the China Meteorological Administration (CMA). However, the forecasts of surface latent heat fluxes and surface air temperature have systematic biases, which [...] Read more.
The GRAPES (Global/Regional Assimilation and Prediction System) global medium-range forecast system (GRAPES_GFS) is a new generation numerical weather forecast model developed by the China Meteorological Administration (CMA). However, the forecasts of surface latent heat fluxes and surface air temperature have systematic biases, which affect the forecasts of atmospheric dynamics by modifying the lower boundary conditions and degrading the application of GRAPES_GFS since the 2 m air temperature is one of the key components of weather forecast products. Here, we add a soil resistance term to reduce soil evaporation, which ultimately reduces the positive forecast bias of the land surface latent heat flux. We also reduce the positive forecast bias of the ocean surface latent heat flux by considering the effect of salinity in the calculation of the ocean surface vapor pressure and by adjusting the parameterizations of roughness length for the exchanges in momentum, heat, and moisture between the ocean surface and atmosphere. Moreover, we modify the parameterization of the roughness length for the exchanges in heat and moisture between the land surface and atmosphere to reduce the cold bias of the nighttime 2 m air temperature forecast over areas with lower vegetation height. We also consider the supercooled soil water to reduce the warm forecast bias of the 2 m air temperature over northern China during winter. These modified parameterizations are incorporated into the GRAPES_GFS and show good performance based on a set of evaluation experiments. This paper highlights the importance of the representations of the land/ocean surface and boundary layer processes in the forecasting of surface heat fluxes and 2 m air temperature. Full article
(This article belongs to the Topic Numerical Models and Weather Extreme Events)
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21 pages, 4828 KiB  
Article
Artificial Neural Network (ANN)-Based Long-Term Streamflow Forecasting Models Using Climate Indices for Three Tributaries of Goulburn River, Australia
by Shamotra Oad, Monzur Alam Imteaz and Fatemeh Mekanik
Climate 2023, 11(7), 152; https://doi.org/10.3390/cli11070152 - 19 Jul 2023
Cited by 3 | Viewed by 1285
Abstract
Water resources systems planning, and control are significantly influenced by streamflow forecasting. The streamflow in northern and north-central regions of Victoria (Australia) is influenced by different climate indices, such as El Niño Southern Oscillation, Interdecadal Pacific Oscillation, Pacific Decadal Oscillation, and Indian Ocean [...] Read more.
Water resources systems planning, and control are significantly influenced by streamflow forecasting. The streamflow in northern and north-central regions of Victoria (Australia) is influenced by different climate indices, such as El Niño Southern Oscillation, Interdecadal Pacific Oscillation, Pacific Decadal Oscillation, and Indian Ocean Dipole. This paper presents the development of the ANN model using machine learning with the multi-layer perceptron and Levenberg algorithm for long-term streamflow forecasting for three tributaries of Goulburn River located within Victoria through establishing relationships between climate indices and streamflow. The climate indices were used as input predictors and the models’ performances were analyzed through best fit correlation. The higher correlation values of the developed models evident from Pearson regression (R) values ranging from 0.61 to 0.95 reveal the models’ acceptability. The accuracies of ANN models were evaluated using statistical measures such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). It is found that considering R, RMSE, MAE and MAPE values, the ENSO has more influence (61% to 95%) on the streamflow of Goulburn River tributaries than other climate drivers. Moreover, it is concluded that Acheron ANN models are the best models that can be confidently used to forecast the streamflow even six-months ahead. Full article
(This article belongs to the Topic Numerical Models and Weather Extreme Events)
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19 pages, 9700 KiB  
Article
Wind Simulations over Western Patagonia Using the Weather Research and Forecasting model and Reanalysis
by Hugo Vásquez Anacona, Cristian Mattar, Nicolás G. Alonso-de-Linaje, Héctor H. Sepúlveda and Jessica Crisóstomo
Atmosphere 2023, 14(7), 1062; https://doi.org/10.3390/atmos14071062 - 23 Jun 2023
Cited by 2 | Viewed by 1265
Abstract
The Chilean Western Patagonia has the highest wind potential resources in South America. Its complex terrain deserves a special attention for wind modeling and assessments. In this work, we have performed a comprehensive meso-scale climate simulation on Weather Research and Forecasting (WRF) in [...] Read more.
The Chilean Western Patagonia has the highest wind potential resources in South America. Its complex terrain deserves a special attention for wind modeling and assessments. In this work, we have performed a comprehensive meso-scale climate simulation on Weather Research and Forecasting (WRF) in order to provide new insights into the wind climatology in Western Patagonia. Simulations were carried out from 1989 to 2020, and we considered a previous sensitivity analysis for their configuration. In situ data from a wind mast, meteorological information and data from eddy flux stations were used to evaluate the results. Reanalysis data from ERA-5, MERRA-2 and RECON80-17 were also used to perform a comparison of the obtained results with the WRF simulation. The results show that the WRF simulation using ERA-5 presented in this work is slightly different to a mathematical reconstruction using MERRA-2 (RECON80-17), which is widely accepted in Chile for wind resource assessments, presenting a statistical difference of about EMD = 0.8 [m s−1] and RMSE = 0.5. Non-significative differences were found between the WRF simulation and MERRA-2 reanalysis, while ERA-5 with MERRA-2 presented a remarkable statistical difference of about EMD = 1.64 [m s−1] and RMSE = 1.8. In relation to flux comparison, reanalysis and WRF in contrast with in situ observations presented a good performance during the summer season, although a spatial resolution bias was noticed. These results can be used as an input for further research related to WRF simulations in Western Patagonia to provide reliable information on wind energy exploration and extreme climatological phenomena such as heat waves. Full article
(This article belongs to the Topic Numerical Models and Weather Extreme Events)
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17 pages, 6673 KiB  
Article
The Impact of Autoconversion Parameterizations of Cloud Droplet to Raindrop on Numerical Simulations of a Meiyu Front Heavy Rainfall Event
by Zhaoping Kang, Zhimin Zhou, Yuting Sun, Yang Hu and Dengxin He
Atmosphere 2023, 14(6), 1001; https://doi.org/10.3390/atmos14061001 - 09 Jun 2023
Cited by 1 | Viewed by 1056
Abstract
This study analyzes the different impacts of autoconversion of cloud droplets to raindrops (ACR) in a Meiyu front rainfall event by comparing two simulations using different parameterizations (KK00 and LD04) in the Weather Research and Forecasting (WRF) model. The Meiyu frontal clouds are [...] Read more.
This study analyzes the different impacts of autoconversion of cloud droplets to raindrops (ACR) in a Meiyu front rainfall event by comparing two simulations using different parameterizations (KK00 and LD04) in the Weather Research and Forecasting (WRF) model. The Meiyu frontal clouds are further classified into stratiform and deep-convective cloud categories, and the precipitation and microphysical characteristics of the two simulations are compared with a budget analysis of raindrops. The simulated precipitation, radar composite reflectivity distribution, and rain rate evolution are overall consistent with observations while precipitation is overestimated, especially in the rainfall centers. The intensity and vertical structure of the ACR process between the two simulations are significantly different. The ACR rate in LD04 is larger than that in KK00 and there are two peak heights in LD04 but only one in KK00. Accretion of droplets by raindrops (CLcr), melting of ice-phase particles (ML), evaporation of raindrops (VDrv), and accretion of raindrops by ice-phase particles (CLri) are the dominant pathways to raindrop production. Limited distributional differences can be found in both the deep-convective and stratiform clouds between the two simulations during the growth stage of the Meiyu event. Stronger ACR in LD04 results in less cloud droplet content (Lc), more raindrop content (Lr), and larger raindrop number concentration (Nr) and the effect of ACR on Nr is greater than that on Lr. The ACR process also impacts other microphysical processes indirectly, and the influences vary in the two cloud categories. Less CLcr (especially), ML, and VDrv content, caused by stronger ACR, lead to less raindrop production in the LD04 deep-convective clouds, which is different from stratiform clouds, and finally correct the overestimated rainfall center to better match the observations. Full article
(This article belongs to the Topic Numerical Models and Weather Extreme Events)
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20 pages, 9843 KiB  
Article
Impact of Global Warming on Tropical Cyclone Track and Intensity: A Numerical Investigation
by Zhihao Feng, Jian Shi, Yuan Sun, Wei Zhong, Yixuan Shen, Shuo Lv, Yao Yao and Liang Zhao
Remote Sens. 2023, 15(11), 2763; https://doi.org/10.3390/rs15112763 - 25 May 2023
Viewed by 1536
Abstract
Despite numerous studies, the impact of global warming on the tropical cyclone (TC) track and intensity by reasons of data inhomogeneity in remote sensing and large natural variability over a relatively short period of observation is still controversial. Three carbon-emission sensitivity experiments are [...] Read more.
Despite numerous studies, the impact of global warming on the tropical cyclone (TC) track and intensity by reasons of data inhomogeneity in remote sensing and large natural variability over a relatively short period of observation is still controversial. Three carbon-emission sensitivity experiments are conducted to investigate how TC track and intensity respond to changes in the oceanic and atmospheric environment under global warming. The results show a high sensitivity of the simulated TC track and intensity to global warming. On one hand, with increase in carbon emissions, the western Pacific subtropical high expands notably, increasing the poleward steering flow and eventually leading to a poleward shift of TC. On the other hand, the underlying sea-surface temperature and surface-entropy flux increase and, thus, favor the convections near the eyewall. Moreover, the TC structure becomes more upright, which is closely related to the larger pressure gradient near the eyewall. As a result, TC intensity increases with carbon emissions. However, this increase is notably smaller than the maximum potential intensity theory as the TC intensity can reach a threshold if carbon emission still increases in the future. The involved mechanisms on the changes of TC track and intensity are also revealed. Full article
(This article belongs to the Topic Numerical Models and Weather Extreme Events)
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