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Open research questions in Meteorological Phenomena and Simulations

210 unresolved questions extracted from the limitations and future-work sections of 740 Meteorological Phenomena and Simulations papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • The severe imbalance of thunderstorm samples, with only 5% of samples being positive (thunderstorm) and 95% being negative (non-thunderstorm). The need to capture temporal evolution characteristics of meteorological physical quantities. The challenge of achieving stable generalization performance under unbalanced samples.

    Single-Point Thunderstorm Forecasting Based on Second-Order Moist Potential Vorticity and Deep Learning · 2026 · DOI

  • The current deep learning-based lightning forecasting has a short valid period, mostly relying on satellite imagery, radar echoes, and lightning location data. The longest valid period does not exceed 6 h, and the forecasting accuracy is not high.

    Single-Point Thunderstorm Forecasting Based on Second-Order Moist Potential Vorticity and Deep Learning · 2026 · DOI

  • Rapid evolution of convective cores. Strong diurnal variability and sensitivity to environmental triggers. Limited predictability in data-sparse regions.

    Object‐based deep learning for probabilistic convective‐core nowcasting from satellite data · 2026 · DOI

  • The model is limited to predicting convective-core occurrence up to six hours ahead, - The approach relies on satellite data, which may be limited in certain regions, - The model is not explicitly designed to handle complex meteorological phenomena

    Object‐based deep learning for probabilistic convective‐core nowcasting from satellite data · 2026 · DOI

  • Whether these discontinuities leave a downstream forecast-skill signature distinguishable from ordinary lead-time dependence remains unclear.

    The impact of ERA5 assimilation-window discontinuities on convection-permitting forecasts of extreme precipitation: a case study of the July 2023 North China heavy rainfall · 2026 · DOI

  • None of these studies evaluate machine learning weather forecasting models on probabilistic or ensemble predictions across multiple geographic regions; existing work focuses either on deterministic single-location forecasts or probabilistic forecasts at global scale without regional validation, leaving a gap in probabilistic regional weather prediction.

    Probabilistic weather forecasting with machine learning · 2024 · DOI

  • During rotorcraft ship-deck landing operations, complex interactional aerodynamic phenomena occur between the rotor and ship airwakes which are not fully understood.

    Mid-fidelity NATO generic destroyer static and moving-ship airwake simulations using the Lattice-Boltzmann method compared with experimental data · 2024 · DOI

  • However, there has been limited research focused on the lower-tropospheric wind fields in the Qinghai-Tibet Plateau.

    Investigating Wind Characteristics and Temporal Variations in the Lower Troposphere over the Northeastern Qinghai–Tibet Plateau Using a Doppler LiDAR · 2024 · DOI

  • Further studies can apply the ICON modeling framework and icemode implementation to other convective systems - The study's approach can be used to examine other aspects of ice microphysics processes

    Convection over Southern Scandinavia: a Modeling Perspective · 2026 · DOI

  • There is a lack of understanding of ice microphysics processes in convective systems - Prior studies have not examined the importance of each formation pathway by altitude and convective stage

    Convection over Southern Scandinavia: a Modeling Perspective · 2026 · DOI

  • The chaotic nature of the atmosphere reduces the usefulness of the information contained in atmospheric initial conditions for increasing lead times - Accurately incorporating land surface anomalies remains challenging and can degrade model performance

    Enhancing subseasonal forecasting skill with land observations and physics-informed deep learning · 2026 · DOI

  • Most operational weather forecast models represent only the mean seasonal cycle of land conditions - Accurately incorporating land surface anomalies remains challenging and can degrade model performance

    Enhancing subseasonal forecasting skill with land observations and physics-informed deep learning · 2026 · DOI

  • The lack of pedagogical frameworks that balance computational depth with accessibility in vocational STEM education. The need for integrating locally collected environmental data into vocational secondary education.

    Developing Data Literacy through Real-Time Environmental Monitoring in Vocational STEM: A Dual-Tool Framework Integrating Atmospheric Physics and Computational Thinking · 2026 · DOI

  • The quantitative relationship between atmospheric stability indices (CAPE, CIN, KI, TTI) and the intensity of downbursts in this region needs to be established through comparative analysis across multiple events.

    Meteorological Analysis of Strong Wind-Producing Clouds: A Case Study of Klaten, 18 November 2024 · 2026 · DOI

  • The study assumes hydrostatic equilibrium state and stable wind shear and thermal stratification. The study assumes the systematic errors of observation instruments remain relatively stable.

    Optimization Study of Low-Altitude Turbulence Intensity Modeling Based On TKE-XGBoost · 2026 · DOI

  • Further development of turbulence intensity modeling methods using machine learning algorithms. Application of the proposed method to other fields such as hydrometeorology and atmospheric dynamics.

    Optimization Study of Low-Altitude Turbulence Intensity Modeling Based On TKE-XGBoost · 2026 · DOI

  • Further study of the interaction between the low-level jet and the topography of Law Dome. Development of more accurate weather forecasting models that consider mesoscale processes.

    The record windstorm event at Casey Station, Antarctica, 5–6 January 2022 · 2026 · DOI

  • The need to understand the mechanisms responsible for extreme weather events. The importance of considering mesoscale processes in predicting extreme weather events.

    The record windstorm event at Casey Station, Antarctica, 5–6 January 2022 · 2026 · DOI

  • The model presents limitations in predicting very high RHi values (above 120 %). The limited availability of IAGOS measurements in certain regions may impact model performance.

    Technical note: Hybrid machine learning model for bias correction of UTLS relative humidity against IAGOS observations in ERA5 reanalysis · 2026 · DOI

  • Accurate prediction of RHi distribution within ISSRs at cruising altitude remains difficult. Reanalysis data, such as ERA5, suffer from a dry bias near the tropopause.

    Technical note: Hybrid machine learning model for bias correction of UTLS relative humidity against IAGOS observations in ERA5 reanalysis · 2026 · DOI

  • Existing monitoring methods have limited recognition accuracy and timely warning. The need for a novel tornado detection algorithm that can improve detection probability and lead time.

    TDA-DARKNet: A Deep Learning Model Based on Dual-Polarization Radar Data for Tornado Detection · 2026 · DOI

  • The study uses a limited dataset, with forecasts evaluated using independent boundary-layer jet events from April to June during 2020-2024. The AI model provides only 13 vertical levels, which may limit its ability to fully capture the vertical structure of boundary-layer jets.

    Complementary error structures of AI and numerical models in forecasting boundary-layer jets over the South China Sea · 2026 · DOI

  • Further evaluation of the U-Net-based blending framework using a larger dataset. Application of the blending framework to other weather forecasting applications. Investigation of the potential of hybrid forecasting systems for other types of weather extremes.

    Complementary error structures of AI and numerical models in forecasting boundary-layer jets over the South China Sea · 2026 · DOI

  • State-of-the-art ML models produce smoother outputs that lack physical consistency at short (<300 km) spatial scales. The smoothing compounds over time, resulting in forecasts that are less useful in some downstream applications. The ensemble of ML predictions does not cover the range of plausible physical scenarios.

    ArchesWeatherGen: Skillful and compute-efficient probabilistic weather forecasting with machine learning · 2026 · DOI

  • Investigating the discrepancy in performance between ArchesWeatherGen and Stormer. Exploring the use of other datasets and models to further improve the performance of ArchesWeatherGen.

    ArchesWeatherGen: Skillful and compute-efficient probabilistic weather forecasting with machine learning · 2026 · DOI

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210 open questions have been extracted from the limitations and future-work passages of 740 Meteorological Phenomena and Simulations papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

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