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Atmosphere
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Numerical Weather Prediction Models and Ensemble Prediction Systems (2nd Edition)
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Numerical Weather Prediction Models and Ensemble Prediction Systems (2nd Edition)

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

Deadline for manuscript submissions: 30 July 2026 | Viewed by 8053

Special Issue Editor

Dr. Petroula Louka

E-Mail Website
Guest Editor
Department of Mathematics and Natural Sciences, Hellenic Air Force Academy, Athens, Greece
Interests: NWP models; EPS; evaluation of NWPs and EPSs; aviation meteorology; effects of weather on aviation with emphasis on convective systems, icing, turbulence, dust transfer; study of convective systems with the use of NWPs, radar and satellite data; atmospheric boundary-layer meteorology
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Special Issue Information

Dear Colleagues,

This Special Issue is the second volume in a series of publications dedicated to “Numerical Weather Prediction Models and Ensemble Prediction Systems” (https://www.mdpi.com/journal/atmosphere/special_issues/54WIE005M5).

Short- to medium-range weather forecasting is based both on high-resolution Numerical Weather Prediction (NWP) models that are able to accurately represent certain atmospheric processes, presenting the deterministic approach, and on the probabilistic Ensemble Prediction Systems (EPSs) that provide information on the level of uncertainty in forecasts whose spread is obtained by perturbing both the initial conditions and also aspects of the physical processes within the model. Both require extensive research on the representation of physical processes, numerical methods, and data assimilation methodologies, while objective evaluation systems are necessary to assess their performance.

The aim of this Special Issue is to communicate advances in NWP models and EPS as state-of-the-art weather prediction tools that rely on the development of a seamless earth system modeling framework and that make the best use of the model outputs in an objective way for both research and operational applications, such as in aviation, shipping, emergency warning systems, and renewable energy. Hence, this Special Issue intends to collect contributions on new developments in data assimilation systems and the integration of observing systems to support NWP models, improvements in model physics and parameterizations of subgrid-scale processes, and the adoption of innovative computational grids and numerical methods leading to forecast skill enhancement, as well as statistical approaches to evaluate their impact. In the case of EPS applications, the focus is on perturbation methods of near-convection-permitting systems for developing members with a certain spread of different solutions, the range of which enables the assessment of the uncertainty in the probabilistic forecast and the confidence in the deterministic predictions. The study of high-impact weather events, their evolution, and the analysis of dynamical and physical characteristics through NWP applications is also encouraged.

Dr. Petroula Louka
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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

  • model physics
  • model parameterizations
  • subgrid-scale processes
  • perturbation methods
  • eps spread
  • data assimilation systems
  • model evaluation
  • high-impact weather events
  • NWP and EPS applications

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Published Papers (4 papers)

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Research

18 pages, 7950 KB  
Article
Comparative Evaluation of ecPoint and EMOS for CMA-GEPS Precipitation Forecast over Eastern China
by Sonum Stejik, Phuntsok Tsewang, Pu Liu and Jialing Wang
Atmosphere 2026, 17(5), 458; https://doi.org/10.3390/atmos17050458 - 30 Apr 2026
Viewed by 320
Abstract
Post-processing of numerical weather prediction (NWP) models constitutes a pivotal link in enhancing forecast performance. Despite their recognition as cutting-edge point-based post-processing techniques, systematic comparative evaluations of ecPoint (ECWMF for point forecasts) and Ensemble Model Output Statistics (EMOS)—particularly assessments of their applicability outside [...] Read more.
Post-processing of numerical weather prediction (NWP) models constitutes a pivotal link in enhancing forecast performance. Despite their recognition as cutting-edge point-based post-processing techniques, systematic comparative evaluations of ecPoint (ECWMF for point forecasts) and Ensemble Model Output Statistics (EMOS)—particularly assessments of their applicability outside Europe and to Chinese ensemble forecasting systems—remain sparse. In this study, we evaluate two advanced post-processing techniques—EMOS and the ecPoint—for calibrating ensemble precipitation forecasts. A comprehensive assessment of the performance of these ensemble post-processing methods is conducted using the CMA-GEPS (China Meteorological Administration’s Global Ensemble Forecasting System forecast over eastern China. The results demonstrate that both methods significantly mitigate systematic biases and improve the reliability and dispersion of ensemble forecasts. Notably, improvement in forecast accuracy is observed even under convective weather conditions and early-warning capability of extreme precipitation events is improved. Overall, while both methods show comparable performance, they exhibit distinct behaviours across different regions. The ecPoint method slightly outperforms EMOS in terms of Continuous Ranked Probability Score (CRPS) and provides improved resolution and early-warning capabilities at various precipitation thresholds. Full article
(This article belongs to the Special Issue Numerical Weather Prediction Models and Ensemble Prediction Systems (2nd Edition))
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16 pages, 5649 KB  
Article
Improving Probabilistic Lightning Forecasts Through Ensemble Postprocessing with Mesoscale Information
by Haoyue Li, Ziqiang Huo and Jialing Wang
Atmosphere 2026, 17(4), 371; https://doi.org/10.3390/atmos17040371 - 3 Apr 2026
Viewed by 381
Abstract
Accurate short-term lightning forecasting requires reliable representations of both lightning occurrence and intensity, as well as the underlying convective processes. While ensemble prediction systems (EPSs) provide valuable probabilistic information, their ability to resolve mesoscale and convective-scale variability remains limited. In this study, we [...] Read more.
Accurate short-term lightning forecasting requires reliable representations of both lightning occurrence and intensity, as well as the underlying convective processes. While ensemble prediction systems (EPSs) provide valuable probabilistic information, their ability to resolve mesoscale and convective-scale variability remains limited. In this study, we assess the added value of mesoscale information for probabilistic lightning forecasting over eastern China. A mesoscale ensemble is constructed from deterministic forecasts of the China Meteorological Administration (CMA) Mesoscale Model (MESO) using spatiotemporal neighborhood and time-lagged techniques and is combined with predictors from the CMA Regional Ensemble Prediction System (REPS). Lightning occurrence and counts are modeled within a Bayesian additive model for location, scale, and shape (BAMLSS) framework, using a hurdle-based count regression to account for excess zeros and overdispersion. Influential nonlinear predictors are selected via stability selection combined with gradient boosting. Forecast performance with and without MESO-derived predictors is systematically evaluated. The results indicate that incorporating mesoscale information generally improves forecast skill for both lightning occurrence and intensity across multiple verification metrics. These improvements are associated with MESO-derived predictors related to convective available potential energy and convective precipitation, suggesting the importance of mesoscale processes for probabilistic lightning forecasting. Full article
(This article belongs to the Special Issue Numerical Weather Prediction Models and Ensemble Prediction Systems (2nd Edition))
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21 pages, 11264 KB  
Article
Comparative Analysis of Perturbation Characteristics Between LBGM and ETKF Initial Perturbation Methods in Convection-Permitting Ensemble Forecasts
by Jiajun Li, Chaohui Chen, Xiong Chen, Hongrang He, Yongqiang Jiang and Yanzhen Kang
Atmosphere 2025, 16(6), 744; https://doi.org/10.3390/atmos16060744 - 18 Jun 2025
Viewed by 1022
Abstract
This study investigates an extreme squall line event that occurred in northern Jiangxi Province, China on 30–31 March 2024. Based on the WRF model, convection-permitting ensemble forecast experiments were conducted using two distinct initial perturbation approaches, namely, the Local Breeding of Growing Modes [...] Read more.
This study investigates an extreme squall line event that occurred in northern Jiangxi Province, China on 30–31 March 2024. Based on the WRF model, convection-permitting ensemble forecast experiments were conducted using two distinct initial perturbation approaches, namely, the Local Breeding of Growing Modes (LBGM) and the Ensemble Transform Kalman Filter (ETKF), to compare their perturbation structures, spatiotemporal evolution, and precipitation forecasting capabilities. The experiments demonstrated the following: (1) The LBGM method significantly improved the root mean square error (RMSE) of mid-upper tropospheric variables, particularly demonstrating superior performance in low-level temperature field forecasts, but the overall ensemble spread of the system was consistently smaller than that of ETKF. (2) The evolution of dynamical spread within the squall line system confirmed that ETKF generated greater spread growth in low-level wind fields, while LBGM exhibited better spatiotemporal alignment between mid-upper tropospheric wind field spread and the synoptic system evolution. (3) Vertical profiles of total moist energy revealed that ETKF initially exhibited higher total moist energy than LBGM. Both methods showed increasing total moist energy with forecast lead time, displaying a bimodal structure dominated by kinetic energy in upper layers (300–100 hPa) and balanced kinetic energy and moist physics terms in lower layers (1000–700 hPa), with ETKF demonstrating larger growth rates. (4) Kinetic energy spectrum analysis indicated that ETKF exhibited significantly higher perturbation energy than LBGM in the 100–1000 km mesoscale range and superior small- to medium-scale perturbation characterization at the 6–60 km scales initially. Precipitation and radar echo verification showed that ETKF effectively corrected positional biases in precipitation forecasts, while LBGM more accurately reproduced the bow-shaped echo structure near Nanchang due to its precise simulation of leading-edge vertical updrafts and rear-sector low pseudo-equivalent potential temperature regions. Full article
(This article belongs to the Special Issue Numerical Weather Prediction Models and Ensemble Prediction Systems (2nd Edition))
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18 pages, 2624 KB  
Article
Performance Evaluation of Numerical Weather Prediction Models in Forecasting Rainfall Events in Kerala, India
by V. Nitha, S. K. Pramada, N. S. Praseed and Venkataramana Sridhar
Atmosphere 2025, 16(4), 372; https://doi.org/10.3390/atmos16040372 - 25 Mar 2025
Cited by 8 | Viewed by 5229
Abstract
Heavy rainfall events are the main cause of flooding, especially in regions like Kerala, India. Kerala is vulnerable to extreme weather due to its geographical location in the Western Ghats. Accurate forecasting of rainfall events is essential for minimizing the impact of floods [...] Read more.
Heavy rainfall events are the main cause of flooding, especially in regions like Kerala, India. Kerala is vulnerable to extreme weather due to its geographical location in the Western Ghats. Accurate forecasting of rainfall events is essential for minimizing the impact of floods on life, infrastructure, and agriculture. For accurate forecasting of heavy rainfall events in this region, region-specific evaluations of NWP model performance are very important. This study evaluated the performance of six Numerical Weather Prediction (NWP) models—NCEP, NCMRWF, ECMWF, CMA, UKMO, and JMA—in forecasting heavy rainfall events in Kerala. A comprehensive assessment of these models was performed using traditional performance metrics, categorical precipitation metrics, and Fractional Skill Scores (FSSs) across different forecast lead times. FSSs were calculated for different rainfall thresholds (100 mm, 50 mm, 5 mm). The results reveal that all models captured rainfall patterns well for the lower threshold of 5 mm, but most of the models struggled to accurately forecast heavy rainfall, especially for longer lead times. JMA performed well overall in most of the metrics except False Alarm Ratio (FAR). It showed high FAR, which revealed that it may predict false rainfall events. ECMWF demonstrated consistent performance. NCEP and UKMO performed moderately well. CMA, and NCMRWF had the lowest accuracy either due to more errors or biases. The findings underscore the trade-offs in model performance, suggesting that model selection should depend on the accuracy required or rainfall event prediction capability. This study recommends the use of Multi-Model Ensembles (MME) to improve forecasting accuracy, integrate the strengths of the best-performing models, and reduce biases. Future research can also focus on expanding observational networks and employing advanced data assimilation techniques for more reliable predictions, particularly in regions with complex terrain such as Kerala. Full article
(This article belongs to the Special Issue Numerical Weather Prediction Models and Ensemble Prediction Systems (2nd Edition))
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