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Advances in Mesoscale Numerical Weather Prediction and its Applications

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

Deadline for manuscript submissions: closed (1 July 2021) | Viewed by 31508

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Dr. Anastasios Papadopoulos

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Guest Editor

Hellenic Centre for Marine Research, 19013 Athens, Greece
Interests: numerical weather prediction; model evaluation; operational meteorology and hydrometeorology; water and energy cycle; land/sea–air interactions; flash floods; coupling numerical models
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Dr. George Varlas

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Guest Editor

Hellenic Centre for Marine Research, 19013 Athens, Greece
Interests: numerical weather prediction; synoptic and dynamic meteorology; boundary layer; hydrometeorological modelling; extreme weather events; flash floods; coupling numerical models; land/sea-air interaction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advances in computer science and information technology facilitates continuing progress in mesoscale numerical weather prediction (NWP) for both research and operational forecasting purposes. Nowadays, mesoscale NWP models are essential in a broad spectrum of applications ranging from driving other numerical models (related to, e.g., ocean, hydrology, and air quality) and downscaling climate simulations to providing forecasts for renewable energy and hydrological purposes.

This Special Issue aims to shed new light on interdisciplinary applications of mesoscale NWP to reveal weather-related physical processes as well as to mitigate the consequences of high-impact weather events. Therefore, this Special Issue intends to collect contributions that report advancements and the current state of the mesoscale NWP models, including forecast skill improvements, the impact of physical parameterizations on forecasts, the validation and intercomparison of forecasts, as well as applications of data assimilation techniques and nowcasting methods. Furthermore, this Special Issue welcomes numerical experiments and case studies regarding strategies designed to couple mesoscale NWP models with hydrological, ocean, wave, and chemical models to improve our understanding of the mesoscale physical and dynamical processes that can trigger natural hazards. Methodological approaches and applications exploiting this knowledge to advance analyses and forecasts as well as to make them tailored for the design and refinement of early warning systems and decision support services are of particular interest.

Dr. Anastasios Papadopoulos
Dr. George Varlas
Guest Editors

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Keywords

Mesoscale meteorology
Numerical weather prediction (NWP)
Convection-permitting NWP
Physical parameterizations
Ensemble weather forecasting
Coupled models
Hydrometeorological simulations
Land/sea–atmosphere interactions
Sensitivity experiments
Data assimilation
Model verification
Tropical-like Mediterranean cyclones
Severe local storms
Weather-induced wildfires
Wind power forecasting
Early warning systems
Decision support systems

Related Special Issues

Advances in Mesoscale Numerical Weather Prediction and Its Applications (2nd Volume) in Atmosphere (9 articles)
Advances in Mesoscale Numerical Weather Prediction and Its Applications (3rd Edition) in Atmosphere (1 article)

Published Papers (11 papers)

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Research

14 pages, 5796 KiB

Open AccessArticle

A Modeling Study on the Downslope Wind of “Katevatos” in Greece and Implications for the Battle of Arachova in 1826

by Stavros Solomos, Panagiotis T. Nastos, Dimitrios Emmanouloudis, Antonia Koutsouraki and Christos Zerefos

Atmosphere 2021, 12(8), 993; https://doi.org/10.3390/atmos12080993 - 31 Jul 2021

Cited by 1 | Viewed by 2452

Abstract

Downslope winds and lee gravity waves are common features of mountainous environments. A similar weather type at Mt. Parnassos in Arachova, Greece is known as “Katevatos” and has devastating results for the population and visitors at the local touristic resorts. In this study, we analyze three incidents of this atmospheric pattern at local scale resolution (1 × 1 km) with WRF model. This is the first study of this local weather hazard, and the following key factors are identified. (I) The main synoptic forcing is the propagation of an upper-level trough from central Europe towards the Balkans. (II) The associated generation of a surface low-pressure system over the Aegean Sea results in a northeast flow in the lower troposphere that is perpendicular to the main topographic ridge of Mt. Parnassos. (III) Generation of gravity waves and downward reflection of wave energy at the critical level between the upper level flow and the undercutting northeast current result in the formation of “Katevatos” downslope wind at the lee side of the mountain. This hurricane-scale wind is accompanied with horizontal transport of frozen rain and snow from the mountain tops towards the village of Arachova. This wind pattern appeared also during the battle of Arachova in November 1826 between the Greek and Ottoman forces resulting in enormous casualties due to the adverse weather conditions. Full article

(This article belongs to the Special Issue Advances in Mesoscale Numerical Weather Prediction and its Applications)

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24 pages, 12261 KiB

Open AccessArticle

Evaluating the Forecast Skill of a Hydrometeorological Modelling System in Greece

by George Varlas, Anastasios Papadopoulos, George Papaioannou and Elias Dimitriou

Atmosphere 2021, 12(7), 902; https://doi.org/10.3390/atmos12070902 - 13 Jul 2021

Cited by 10 | Viewed by 2671

Abstract

A hydrometeorological forecasting system has been operating at the Institute of Marine Biological Resources and Inland Waters (IMBRIW) of the Hellenic Centre for Marine Research (HCMR) since September 2015. The system consists of the Advanced Weather Research and Forecasting (WRF-ARW) model, the WRF-Hydro hydrological model, and the HEC-RAS hydraulic–hydrodynamic model. The system provides daily 120 h weather forecasts focusing on Greece (4 km horizontal resolution) and hydrological forecasts for the Spercheios and Evrotas rivers in Greece (100 m horizontal resolution), also providing flash flood inundation forecasts when needed (5 m horizontal resolution). The main aim of this study is to evaluate precipitation forecasts produced in a 4-year period (September 2015–August 2019) using measurements from meteorological stations across Greece. Water level forecasts for the Evrotas and Spercheios rivers were also evaluated using measurements from hydrological stations operated by the IMBRIW. Moreover, the forecast skill of the chained meteorological–hydrological–hydraulic operation of the system was investigated during a catastrophic flash flood in the Evrotas river. The results indicated that the system provided skillful precipitation and water level forecasts. The best evaluation results were yielded during rainy periods. They also demonstrated that timely flash flood forecasting products could benefit flood warning and emergency responses due to their efficiency and increased lead time. Full article

(This article belongs to the Special Issue Advances in Mesoscale Numerical Weather Prediction and its Applications)

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16 pages, 1350 KiB

Open AccessArticle

The Impact of the Variation in Weather and Season on WRF Dynamical Downscaling in the Pearl River Delta Region

by Chengwei Zhang, Jianjun He, Xin Lai, Yilin Liu, Huizheng Che and Sunling Gong

Atmosphere 2021, 12(3), 409; https://doi.org/10.3390/atmos12030409 - 21 Mar 2021

Cited by 3 | Viewed by 2277

Abstract

In this study, National Centers for Environmental Prediction (NCEP) Final (FNL) operational global analysis data and meteorological observation data from 2013 to 2017 were used to evaluate the impact of seasonal changes and different circulation classifications on the dynamical downscaling simulation results of Weather Research and Forecasting (WRF) in the Pearl River Delta (PRD) region. The results show that the dynamical downscaling method can accurately simulate the time variation characteristics of the near-surface meteorological field and the hit rates of a 2-m temperature, 2-m relative humidity, 10-m wind speed, and 10-m wind direction are 92.66%, 93.98%, 26.78%, and 76.78%, respectively. The WRF model slightly underestimates the temperature and relative humidity, and overestimates the wind speed and precipitation. For precipitation, the WRF model can better simulate the variation characteristics of light rain and heavy rain, with the probability of detection are 0.59 and 0.69, respectively. For seasonal factors, the WRF model can conduct a perfect simulation in autumn and winter, followed by spring, while summer is vulnerable to extreme weather, so the result of the simulation is relatively poor. The circulation type is an important parameter of downscaling assessment. When the PRD is controlled by high pressure, the simulated results of WRF are good, and when the PRD is affected by low pressure or extreme weather, the simulation results are relatively poor. Full article

(This article belongs to the Special Issue Advances in Mesoscale Numerical Weather Prediction and its Applications)

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17 pages, 14756 KiB

Open AccessArticle

Concurrent Influence of Different Natural Sources on the Particulate Matter in the Central Mediterranean Region during a Wildfire Season

by Jessica Castagna, Alfonso Senatore, Mariantonia Bencardino and Giuseppe Mendicino

Atmosphere 2021, 12(2), 144; https://doi.org/10.3390/atmos12020144 - 23 Jan 2021

Cited by 4 | Viewed by 2597

Abstract

Wildfire occurrence and severity in the Mediterranean region during the summer season is increasing, being favoured by climate change-induced conditions (i.e., drought, heatwaves). Moreover, additional natural sources frequently impact this region, particularly Saharan dust intrusions. This study focuses on the combined effect of wildfires and Saharan dust on the air quality of the central Mediterranean Basin (CMB) during 2017, an exceptional year for forested burned areas in southern Italy. The annual behaviors of PM_2.5, PM₁₀, CO, benzene, and benzo(a)pirene measurements that were recorded at a rural regional-background station located in southern Italy, highlighted a concentration increase during summer. Both Saharan dust and wildfire events were identified while using Navy Aerosol Analysis and Prediction System (NAAPS) model maps, together with high-resolution Weather Research and Forecast—Hybrid Single-Particle Lagrangian Integrated Trajectory (WRF-HYSPLIT) back-trajectories. Additionally, Visible Infrared Imaging Radiometer Suite (VIIRS) satellite detections were considered to establish the enrichment of air masses by wildfire emissions. Finally, the occurrence of these natural sources, and their influence on particulate matter, were examined. In this case study, both PM_2.5 and PM₁₀ exceedances occurred predominantly in conjunction with wildfire events, while Saharan dust events mainly increased PM₁₀ concentration when overlapping with wildfire effects. Full article

(This article belongs to the Special Issue Advances in Mesoscale Numerical Weather Prediction and its Applications)

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23 pages, 113715 KiB

Open AccessArticle

Evaluation of Two Cloud-Resolving Models Using Bin or Bulk Microphysics Representation for the HyMeX-IOP7a Heavy Precipitation Event

by Diana Arteaga, Céline Planche, Christina Kagkara, Wolfram Wobrock, Sandra Banson, Frédéric Tridon and Andrea Flossmann

Atmosphere 2020, 11(11), 1177; https://doi.org/10.3390/atmos11111177 - 31 Oct 2020

Cited by 1 | Viewed by 1829

Abstract

The Mediterranean region is frequently affected in autumn by heavy precipitation that causes flash-floods or landslides leading to important material damage and casualties. Within the framework of the international HyMeX program (HYdrological cycle in Mediterranean EXperiment), this study aims to evaluate the capabilities of two models, WRF (Weather Research and Forecasting) and DESCAM (DEtailed SCAvenging Model), which use two different representations of the microphysics to reproduce the observed atmospheric properties (thermodynamics, wind fields, radar reflectivities and precipitation features) of the HyMeX-IOP7a intense precipitating event (26 September 2012). The DESCAM model, which uses a bin resolved representation of the microphysics, shows results comparable to the observations for the precipitation field at the surface. On the contrary, the simulations made with the WRF model using a bulk representation of the microphysics (either the Thompson scheme or the Morrison scheme), commonly employed in NWP models, reproduce neither the intensity nor the distribution of the observed precipitation—the rain amount is overestimated and the most intense cell is shifted to the East. The different simulation results show that the divergence in the surface precipitation features seems to be due to different mechanisms involved in the onset of the precipitating system: the convective system is triggered by the topography of the Cévennes mountains (i.e., south-eastern part of the Massif Central) in DESCAM and by a low-level flux convergence in WRF. A sensitivity study indicates that the microphysics properties have impacted the thermodynamics and dynamics fields inducing the low-level wind convergence simulated with WRF for this HyMeX event. Full article

(This article belongs to the Special Issue Advances in Mesoscale Numerical Weather Prediction and its Applications)

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17 pages, 4672 KiB

Open AccessArticle

Multi-Weather Evaluation of Nowcasting Methods Including a New Empirical Blending Scheme

by Hsin-Hung Lin, Chih-Chien Tsai, Jia-Chyi Liou, Yu-Chun Chen, Chung-Yi Lin, Lee-Yaw Lin and Kao-Shen Chung

Atmosphere 2020, 11(11), 1166; https://doi.org/10.3390/atmos11111166 - 29 Oct 2020

Cited by 7 | Viewed by 2428

Abstract

This study utilized a radar echo extrapolation system, a high-resolution numerical model with radar data assimilation, and three blending schemes including a new empirical one, called the extrapolation adjusted by model prediction (ExAMP), to carry out 150 min reflectivity nowcasting experiments for various heavy rainfall events in Taiwan in 2019. ExAMP features full trust in the pattern of the extrapolated reflectivity with intensity adjustable by numerical model prediction. The spatial performance for two contrasting events shows that the ExAMP scheme outperforms the others for the more accurate prediction of both strengthening and weakening processes. The statistical skill for all the sampled events shows that the nowcasts by ExAMP and the extrapolation system obtain the lowest and second lowest root mean square errors at all the lead time, respectively. In terms of threat scores and bias scores above certain reflectivity thresholds, the ExAMP nowcast may have more grid points of misses for high reflectivity in comparison to extrapolation, but serious overestimation among the points of hits and false alarms is the least likely to happen with the new scheme. Moreover, the event type does not change the performance ranking of the five methods, all of which have the highest predictability for a typhoon event and the lowest for local thunderstorm events. Full article

(This article belongs to the Special Issue Advances in Mesoscale Numerical Weather Prediction and its Applications)

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16 pages, 4931 KiB

Open AccessArticle

Impacts of Multiple Radiance Data Assimilation on the Simulation of Typhoon Chan-Hom

by Dongmei Xu, Aiqing Shu, Feifei Shen, Jinzhong Min, Hong Li and Xiaoli Xia

Atmosphere 2020, 11(9), 957; https://doi.org/10.3390/atmos11090957 - 08 Sep 2020

Cited by 4 | Viewed by 2118

Abstract

With the module of assimilating AMSU-A (Advanced Microwave Sounding Unit-A) and AIRS (Atmospheric Infrared Sounder) data in the WRFDA (Weather Research and Forecasting Model Data Assimilation) system, the impacts of joint assimilation of the radiance observations from two satellites on the simulation of typhoon Chan-hom (2015) are addressed. For comparison, experiments with the assimilation of solely GTS (Global Telecommunications System) data, AMSU-A data, or AIRS data are also performed. The results show that, compared to other experiments, the analysis field after assimilating multiple radiance data is closer to the observation. The simulated steering flow in its forecast field is conductive to the northeast twist of the typhoon. In addition, the simulated rainband and the FSS (fraction skill score) calculated from the experiment with assimilating multiple radiance data are better. In the deterministic forecast, better performance is obtained from the simulation with multiple radiance data in the forecast of track, MSLP (minimum sea level pressure), and MSW (maximum surface wind). Full article

(This article belongs to the Special Issue Advances in Mesoscale Numerical Weather Prediction and its Applications)

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23 pages, 10901 KiB

Open AccessArticle

Flash Flood and Extreme Rainfall Forecast through One-Way Coupling of WRF-SMAP Models: Natural Hazards in Rio de Janeiro State

by Priscila da Cunha Luz Barcellos and Marcio Cataldi

Atmosphere 2020, 11(8), 834; https://doi.org/10.3390/atmos11080834 - 07 Aug 2020

Cited by 19 | Viewed by 3029

Abstract

Flash floods and extreme rains are destructive phenomena and difficult to forecast. In 2011, the mountainous region of Rio de Janeiro state suffered one of the largest natural hazards in Brazil, affecting more than 300,000 people, leaving more than 900 dead. This article simulates this natural hazard through Quantitative Precipitation Forecasting (QPF) and streamflow forecast ensemble, using 18 combinations of parameterizations between cumulus, microphysics, surface layer, planetary boundary layer, land surface and lateral contour conditions of the Weather Research and Forecasting (WRF) Model, coupling to the Soil Moisture Accounting Procedure (SMAP) hydrological model, seeking to find the best set of parametrizations for the forecasting of extreme events in the region. The results showed rainfall and streamflow forecast were underestimated by the models, reaching an error of 57.4% to QPF and 24.6% error to streamflow, and part of these errors are related to the lack of skill of the atmospheric model in predicting the intensity and the spatial-temporal distribution of rainfall. These results bring to light the limitations of numerical weather prediction, possibly due to the lack of initiatives involving the adaptation of empirical constants, intrinsic in the parametrization models, to the specific atmospheric conditions of each region of the country. Full article

(This article belongs to the Special Issue Advances in Mesoscale Numerical Weather Prediction and its Applications)

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21 pages, 7790 KiB

Open AccessArticle

Mesoscale Model Simulation of a Severe Summer Thunderstorm in The Netherlands: Performance and Uncertainty Assessment for Parameterised and Resolved Convection

by Gert-Jan Steeneveld and Esther E.M. Peerlings

Atmosphere 2020, 11(8), 811; https://doi.org/10.3390/atmos11080811 - 31 Jul 2020

Cited by 9 | Viewed by 3091

Abstract

On the evening of 23 June 2016 around 18:00 UTC, a mesoscale convective system (MCS) with hail and wind gusts passed the southern province Noord-Brabant in the Netherlands, and caused 675 millions of euros damage. This study evaluates the performance of the Weather Research and Forecasting model with three cumulus parameterisation schemes (Betts–Miller–Janjic, Grell–Freitas and Kain–Fritsch) on a grid spacing of 4 km in the ‘grey-zone’ and with explicitly resolved convection at 2 and 4 km grid spacing. The results of the five experiments are evaluated against observations of accumulated rainfall, maximum radar reflectivity, the CAPE evolution and wind speed. The results show that the Betts–Miller–Janjic scheme is activated too early and can therefore not predict any MCS over the region of interest. The Grell–Freitas and Kain–Fritsch schemes do predict an MCS, but its intensity is underestimated. With the explicit convection, the model is able to resolve the storm, though with a delay and an overestimated intensity. We also study whether spatial uncertainty in soil moisture is scaled up differently using parameterised or explicitly resolved convection. We find that the uncertainty in soil moisture distribution results in larger uncertainty in convective activity in the runs with explicit convection and the Grell–Freitas scheme, while the Kain–Fritsch and Betts–Miller–Janjic scheme clearly present a smaller variability. Full article

(This article belongs to the Special Issue Advances in Mesoscale Numerical Weather Prediction and its Applications)

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39 pages, 15343 KiB

Open AccessArticle

Wind Speed Forecast Based on Post-Processing of Numerical Weather Predictions Using a Gradient Boosting Decision Tree Algorithm

by Wenqing Xu, Like Ning and Yong Luo

Atmosphere 2020, 11(7), 738; https://doi.org/10.3390/atmos11070738 - 12 Jul 2020

Cited by 25 | Viewed by 4947

Abstract

With the large-scale development of wind energy, wind power forecasting plays a key role in power dispatching in the electric power grid, as well as in the operation and maintenance of wind farms. The most important technology for wind power forecasting is forecasting wind speed. The current mainstream methods for wind speed forecasting involve the combination of mesoscale numerical meteorological models with a post-processing system. Our work uses the WRF model to obtain the numerical weather forecast and the gradient boosting decision tree (GBDT) algorithm to improve the near-surface wind speed post-processing results of the numerical weather model. We calculate the feature importance of GBDT in order to find out which feature most affects the post-processing wind speed results. The results show that, after using about 300 features at different height and pressure layers, the GBDT algorithm can output more accurate wind speed forecasts than the original WRF results and other post-processing models like decision tree regression (DTR) and multi-layer perceptron regression (MLPR). Using GBDT, the root mean square error (RMSE) of wind speed can be reduced from 2.7–3.5 m/s in the original WRF result by 1–1.5 m/s, which is better than DTR and MLPR. While the index of agreement (IA) can be improved by 0.10–0.20, correlation coefficient be improved by 0.10–0.18, Nash–Sutcliffe efficiency coefficient (NSE) be improved by −0.06–0.6. It also can be found that the feature which most affects the GBDT results is the near-surface wind speed. Other variables, such as forecast month, forecast time, and temperature, also affect the GBDT results. Full article

(This article belongs to the Special Issue Advances in Mesoscale Numerical Weather Prediction and its Applications)

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18 pages, 1529 KiB

Open AccessArticle

Warm Rain in Southern West Africa: A Case Study at Savè

by Irene Reinares Martínez, Jean-Pierre Chaboureau and Jan Handwerker

Atmosphere 2020, 11(3), 298; https://doi.org/10.3390/atmos11030298 - 19 Mar 2020

Cited by 3 | Viewed by 2514

Abstract

A warm-rain episode over southern West Africa is analyzed using unprecedented X-band radar observations from Savè, Benin and a Large-Eddy Simulation (LES) over a 240 × 240 km

^{2}

domain. While warm rain contributes to 1% of the total rainfall in the LES, [...] Read more.

A warm-rain episode over southern West Africa is analyzed using unprecedented X-band radar observations from Savè, Benin and a Large-Eddy Simulation (LES) over a 240 × 240 km

^{2}

domain. While warm rain contributes to 1% of the total rainfall in the LES, its spatial extent accounts for 24% of the area covered by rainfall. Almost all the warm-rain cells tracked in the observation and the LES have a size between 2 and 10 km and a lifetime varying from 5 to 60 min. During the nighttime, warm-rain cells are caused by the dissipation of large deep-convection systems while during the daytime they are formed by the boundary-layer thermals. The vertical extension of the warm-rain cells is limited by vertical wind shear at their top. In the simulation, their top is 1.6 km higher with respect to the radar observations due to the large-scale environment given by wrong initial conditions. This study shows the challenge of simulating warm rain in southern West Africa, a key phenomenon during the little dry season. Full article

(This article belongs to the Special Issue Advances in Mesoscale Numerical Weather Prediction and its Applications)

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