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 November 2025 | Viewed by 871

Special Issue Editor


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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

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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 (1 paper)

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Research

18 pages, 2624 KiB  
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
Viewed by 498
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
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